Dumb Terminal

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Dumb Terminal

Computer terminal Dumb terminal

The specific meaning of the term dumb terminal can vary depending on the context in which it is used.

Computer terminal Dumb terminal

This type of dumb terminal is still supported on modern Unix-like systems by setting the environment variable TERM to dumb

Computer terminal Dumb terminal

In the broader context that includes all forms of keyboard/screen computer communication devices, including personal computers, diskless workstations, network computers, thin clients, and X terminals, the term dumb terminal is sometimes used to refer to any type of traditional computer terminal that communicates serially over a RS-232 connection that does not locally process data or execute user programs.

Computer terminal Dumb terminal

The term dumb terminal sometimes also refers to public computer terminals that are limited to monochrome text-only capabilities, or to terminals that transmit each character as it is typed rather than waiting until it is polled by a host computer.

Dumb terminal

A terminal that depends on the host computer for its processing power is called a #Dumb terminal|dumb terminal or thin client

Dumb terminal – Dumb and Intelligent Terminals

From the introduction of the IBM 3270, and the VT100|DEC VT100 (1978), the user and programmer could notice significant advantages in VDU technology improvements, yet not all programmers used the features of the new terminals (backward-compatibility in the VT100 and later Televideo terminals, for example, with ‘dumb terminals’ allowed programmers to continue to use older software).

Dumb terminal – Dumb and Intelligent Terminals

Most terminals in the early 1980s, such as ADM-3A, TVI912, Data General D2, DEC VT52, despite the introduction of ANSI terminals in 1978, were essentially dumb terminals, although some of them (such as the later ADM and TVI models) did have a primitive block-send capability

Dumb terminal – Dumb and Intelligent Terminals

Around the mid 1980s intelligent terminals, costing less that most dumb terminals would have a few years earlier, could provide enough user-friendly local editing of data and send the completed form to the main computer

Dumb terminal – Dumb terminal

The specific meaning of the term ‘dumb terminal’ can vary depending on the context in which it is used.

Dumb terminal – Dumb terminal

This type of dumb terminal is still supported on modern Unix-like systems by setting the environment variable TERM to dumb

Data terminal – Dumb terminal

This type of dumb terminal is still supported on modern Unix-like systems by setting the environment variable TERM to dumb

Dumb terminals – Intelligent terminals

From the introduction of the IBM 3270, and the DEC VT100 (1978), the user and programmer could notice significant advantages in VDU technology improvements, yet not all programmers used the features of the new terminals (backward compatibility in the VT100 and later Televideo terminals, for example, with dumb terminals allowed programmers to continue to use older software).

Dumb terminals – Intelligent terminals

Most terminals in the early 1980s, such as ADM-3A, TVI912, Data General D2, DEC VT52, despite the introduction of ANSI terminals in 1978, were essentially dumb terminals, although some of them (such as the later ADM and TVI models) did have a primitive block-send capability.

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Data mining

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Data mining

Data mining

The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use

Data mining

Even the popular book “Data mining: Practical machine learning tools and techniques with Java” (which covers mostly machine learning material) was originally to be named just “Practical machine learning”, and the term “data mining” was only added for marketing reasons

Data mining

Neither the data collection, data preparation, nor result interpretation and reporting are part of the data mining step, but do belong to the overall KDD process as additional steps.

Data mining

The related terms data dredging, data fishing, and data snooping refer to the use of data mining methods to sample parts of a larger population data set that are (or may be) too small for reliable statistical inferences to be made about the validity of any patterns discovered. These methods can, however, be used in creating new hypotheses to test against the larger data populations.

Data mining

Data mining interprets its data into real time analysis that can be used to increase sales, promote new product, or delete product that is not value-added to the company.

Data mining Etymology

Currently, Data Mining and Knowledge Discovery are used interchangeably.

Data mining Background

Data mining is the process of applying these methods with the intention of uncovering hidden patterns in large data sets

Data mining Research and evolution

The premier professional body in the field is the Association for Computing Machinery’s (ACM) Special Interest Group (SIG) on Knowledge Discovery and Data Mining (SIGKDD). Since 1989 this ACM SIG has hosted an annual international conference and published its proceedings, and since 1999 it has published a biannual academic journal titled “SIGKDD Explorations”.

Data mining Research and evolution

Computer science conferences on data mining include:

Data mining Research and evolution

DMKD Conference – Research Issues on Data Mining and Knowledge Discovery

Data mining Research and evolution

ECDM Conference – European Conference on Data Mining

Data mining Research and evolution

ECML-PKDD Conference – European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases

Data mining Research and evolution

EDM Conference – International Conference on Educational Data Mining

Data mining Research and evolution

PAKDD Conference – The annual Pacific-Asia Conference on Knowledge Discovery and Data Mining

Data mining Research and evolution

SSTD Symposium – Symposium on Spatial and Temporal Databases

Data mining Research and evolution

Data mining topics are also present on many data management/database conferences such as the ICDE Conference, SIGMOD Conference and International Conference on Very Large Data Bases

Data mining Process

(5) Interpretation/Evaluation.

Data mining Process

It exists, however, in many variations on this theme, such as the Cross Industry Standard Process for Data Mining (CRISP-DM) which defines six phases:

Data mining Process

(5) Evaluation

Data mining Process

or a simplified process such as (1) , (2) data mining, and (3) results validation.

Data mining Process

Polls conducted in 2002, 2004, and 2007 show that the CRISP-DM methodology is the leading methodology used by data miners. The only other data mining standard named in these polls was SEMMA. However, 3-4 times as many people reported using CRISP-DM. Several teams of researchers have published reviews of data mining process models, and Azevedo and Santos conducted a comparison of CRISP-DM and SEMMA in 2008.

Data mining Pre-processing

Before algorithms can be used, a target data set must be assembled

Data mining

Anomaly detection (Outlier/change/deviation detection) – The identification of unusual data records, that might be interesting or data errors that require further investigation.

Data mining

Association rule learning (Dependency modeling) – Searches for relationships between variables. For example a supermarket might gather data on customer purchasing habits. Using association rule learning, the supermarket can determine which products are frequently bought together and use this information for marketing purposes. This is sometimes referred to as market basket analysis.

Data mining

Clustering – is the task of discovering groups and structures in the data that are in some way or another “similar”, without using known structures in the data.

Data mining

Classification – is the task of generalizing known structure to apply to new data. For example, an e-mail program might attempt to classify an e-mail as “legitimate” or as “spam”.

Data mining

Regression – Attempts to find a function which models the data with the least error.

Data mining

Summarization – providing a more compact representation of the data set, including visualization and report generation.

Data mining Results validation

For example, a data mining algorithm trying to distinguish “spam” from “legitimate” emails would be trained on a training set of sample e-mails

Data mining Results validation

If the learned patterns do not meet the desired , subsequently it is necessary to re-evaluate and change the pre-processing and data mining steps. If the learned patterns do meet the desired , then the final step is to interpret the learned patterns and turn them into knowledge.

Data mining Standards

There have been some efforts to define standards for the data mining process, for example the 1999 European Cross Industry Standard Process for Data Mining (CRISP-DM 1.0) and the 2004 Java Data Mining standard (JDM 1.0). Development on successors to these processes (CRISP-DM 2.0 and JDM 2.0) was active in 2006, but has stalled since. JDM 2.0 was withdrawn without reaching a final draft.

Data mining Standards

As the name suggests, it only covers prediction models, a particular data mining task of high importance to business applications

Data mining Games

for 3×3-chess) with any beginning configuration, small-board dots-and-boxes, small-board-hex, and certain endgames in chess, dots-and-boxes, and hex; a new area for data mining has been opened

Data mining Business

If Walmart analyzed their point-of-sale data with data mining techniques they would be able to determine sales trends, develop marketing campaigns, and more accurately predict customer loyalty

Data mining Business

Once the results from data mining (potential prospect/customer and channel/offer) are determined, this “sophisticated application” can either automatically send an e-mail or a regular mail

Data mining Business

In order to maintain this quantity of models, they need to manage model versions and move on to automated data mining.

Data mining Business

Data mining can also be helpful to human resources (HR) departments in identifying the characteristics of their most successful employees. Information obtained – such as universities attended by highly successful employees – can help HR focus recruiting efforts accordingly. Additionally, Strategic Enterprise Management applications help a company translate corporate-level goals, such as profit and margin share targets, into operational decisions, such as production plans and workforce levels.

Data mining Business

If a clothing store records the purchases of customers, a data mining system could identify those customers who favor silk shirts over cotton ones

Data mining Business

Market basket analysis has also been used to identify the purchase patterns of the Alpha Consumer. Alpha Consumers are people that play a key role in connecting with the concept behind a product, then adopting that product, and finally validating it for the rest of society. Analyzing the data collected on this type of user has allowed companies to predict future buying trends and forecast supply demands.

Data mining Business

Data mining is a highly effective tool in the catalog marketing industry. Catalogers have a rich database of history of their customer transactions for millions of customers dating back a number of years. Data mining tools can identify patterns among customers and help identify the most likely customers to respond to upcoming mailing campaigns.

Data mining Business

Data mining for business applications is a component that needs to be integrated into a complex modeling and decision making process. Reactive business intelligence (RBI) advocates a “holistic” approach that integrates data mining, modeling, and interactive visualization into an end-to-end discovery and continuous innovation process powered by human and automated learning.

Data mining Business

The relation between the quality of a data mining system and the amount of investment that the decision maker is willing to make was formalized by providing an economic perspective on the value of “extracted knowledge” in terms of its payoff to the organization This decision-theoretic classification framework was applied to a real-world semiconductor wafer manufacturing line, where decision rules for effectively monitoring and controlling the semiconductor wafer fabrication line were developed.

Data mining Business

Another implication is that on-line monitoring of the semiconductor manufacturing process using data mining may be highly effective.

Data mining Science and engineering

In recent years, data mining has been used widely in the areas of science and engineering, such as bioinformatics, genetics, medicine, education and electrical power engineering.

Data mining Science and engineering

The data mining method that is used to perform this task is known as multifactor dimensionality reduction.

Data mining Science and engineering

In the area of electrical power engineering, data mining methods have been widely used for condition monitoring of high voltage electrical equipment

Data mining Science and engineering

Data mining methods have also been applied to dissolved gas analysis (DGA) in power transformers. DGA, as a diagnostics for power transformers, has been available for many years. Methods such as SOM has been applied to analyze generated data and to determine trends which are not obvious to the standard DGA ratio methods (such as Duval Triangle).

Data mining Science and engineering

In this way, data mining can facilitate institutional memory.

Data mining Science and engineering

Other examples of application of data mining methods are biomedical data facilitated by domain ontologies, mining clinical trial data, and traffic analysis using SOM.

Data mining Science and engineering

In adverse drug reaction surveillance, the Uppsala Monitoring Centre has, since 1998, used data mining methods to routinely screen for reporting patterns indicative of emerging drug safety issues in the WHO global database of 4.6 million suspected adverse drug reaction incidents. Recently, similar methodology has been developed to mine large collections of electronic health records for temporal patterns associating drug prescriptions to medical diagnoses.

Data mining Human rights

Data mining of government records – particularly records of the justice system (i.e., courts, prisons) – enables the discovery of systemic human rights violations in connection to generation and publication of invalid or fraudulent legal records by various government agencies.

Data mining Medical data mining

In 2011, the case of Sorrell v. IMS Health, Inc., decided by the Supreme Court of the United States, ruled that pharmacies may share information with outside companies. This practice was authorized under the 1st Amendment of the Constitution, protecting the “freedom of speech.”

Data mining Spatial data mining

So far, data mining and Geographic Information Systems (GIS) have existed as two separate technologies, each with its own methods, traditions, and approaches to visualization and data analysis

Data mining Spatial data mining

Data mining offers great potential benefits for GIS-based applied decision-making. Recently, the task of integrating these two technologies has become of critical importance, especially as various public and private sector organizations possessing huge databases with thematic and geographically referenced data begin to realize the huge potential of the information contained therein. Among those organizations are:

Data mining Spatial data mining

offices requiring analysis or dissemination of geo-referenced statistical data

Data mining Spatial data mining

public health services searching for explanations of disease clustering

Data mining Spatial data mining

environmental agencies assessing the impact of changing land-use patterns on climate change

Data mining Spatial data mining

geo-marketing companies doing customer segmentation based on spatial location.

Data mining Spatial data mining

Challenges in Spatial mining: Geospatial data repositories tend to be very large

Data mining Spatial data mining

Developing and supporting geographic data warehouses (GDW’s): Spatial properties are often reduced to simple aspatial attributes in mainstream data warehouses. Creating an integrated GDW requires solving issues of spatial and temporal data interoperability – including differences in semantics, referencing systems, geometry, accuracy, and position.

Data mining Spatial data mining

Geographic data mining methods should recognize more complex geographic objects (i.e., lines and polygons) and relationships (i.e., non-Euclidean distances, direction, connectivity, and interaction through attributed geographic space such as terrain)

Data mining Spatial data mining

Geographic knowledge discovery using diverse data types: GKD methods should be developed that can handle diverse data types beyond the traditional raster and vector models, including imagery and geo-referenced multimedia, as well as dynamic data types (video streams, animation).

Data mining Sensor data mining

By measuring the spatial correlation between data sampled by different sensors, a wide class of specialized algorithms can be developed to develop more efficient spatial data mining algorithms.

Data mining Visual data mining

In the process of turning from analogical into digital, large data sets have been generated, collected, and stored discovering statistical patterns, trends and information which is hidden in data, in order to build predictive patterns. Studies suggest visual data mining is faster and much more intuitive than is traditional data mining. See also Computer vision.

Data mining Music data mining

Data mining techniques, and in particular co-occurrence analysis, has been used to discover relevant similarities among music corpora (radio lists, CD databases) for the purpose of classifying music into genres in a more objective manner.

Data mining Surveillance

Data mining has been used to fight terrorism by the U.S

Data mining Surveillance

In the context of combating terrorism, two particularly plausible methods of data mining are “” and “subject-based data mining”.

Data mining Pattern mining

“Pattern mining” is a data mining method that involves finding existing patterns in data. In this context patterns often means association rules. The original motivation for searching association rules came from the desire to analyze supermarket transaction data, that is, to examine customer behavior in terms of the purchased products. For example, an association rule “beer ? potato chips (80%)” states that four out of five customers that bought beer also bought potato chips.

Data mining Pattern mining

In the context of pattern mining as a tool to identify terrorist activity, the National Research Council provides the following definition: “Pattern-based data mining looks for patterns (including anomalous data patterns) that might be associated with terrorist activity — these patterns might be regarded as small signals in a large ocean of noise.” Pattern Mining includes new areas such a Music Information Retrieval (MIR) where patterns seen both in the temporal and non temporal domains are imported to classical knowledge discovery search methods.

Data mining Subject-based data mining

“Subject-based data mining” is a data mining method involving the search for associations between individuals in data. In the context of combating terrorism, the National Research Council provides the following definition: “Subject-based data mining uses an initiating individual or other datum that is considered, based on other information, to be of high interest, and the goal is to determine what other persons or financial transactions or movements, etc., are related to that initiating datum.”

Data mining Knowledge grid

Knowledge discovery “On the Grid” generally refers to conducting knowledge discovery in an open environment using grid computing concepts, allowing users to integrate data from various online data sources, as well make use of remote resources, for executing their data mining tasks

Data mining Reliability / Validity

Data mining can be misused, and can also unintentionally produce results which appear significant but which do not actually predict future behavior and cannot be reproduced on a new sample of data. See Data dredging.

Data mining Privacy concerns and ethics

In particular, data mining government or commercial data sets for national security or law enforcement purposes, such as in the Total Information Awareness Program or in ADVISE, has raised privacy concerns.

Data mining Privacy concerns and ethics

This is not data mining per se, but a result of the preparation of data before – and for the purposes of – the analysis

Data mining Privacy concerns and ethics

It is recommended that an individual is made aware of the following before data are collected:

Data mining Privacy concerns and ethics

the purpose of the data collection and any (known) data mining projects

Data mining Privacy concerns and ethics

how the data will be used

Data mining Privacy concerns and ethics

who will be able to mine the data and use the data and their derivatives

Data mining Privacy concerns and ethics

the status of security surrounding access to the data

Data mining Privacy concerns and ethics

In America, privacy concerns have been addressed to some extent by the US Congress via the passage of regulatory controls such as the Health Insurance Portability and Accountability Act (HIPAA)

Data mining Privacy concerns and ethics

Data may also be modified so as to become anonymous, so that individuals may not readily be identified. However, even “de-identified”/”anonymized” data sets can potentially contain enough information to allow identification of individuals, as occurred when journalists were able to find several individuals based on a set of search histories that were inadvertently released by AOL.

Data mining Free open-source data mining software and applications

Carrot2: Text and search results clustering framework.

Data mining Free open-source data mining software and applications

Chemicalize.org: A chemical structure miner and web search engine.

Data mining Free open-source data mining software and applications

ELKI: A university research project with advanced cluster analysis and outlier detection methods written in the Java language.

Data mining Free open-source data mining software and applications

GATE: a natural language processing and language engineering tool.

Data mining Free open-source data mining software and applications

KNIME: The Konstanz Information Miner, a user friendly and comprehensive data analytics framework.

Data mining Free open-source data mining software and applications

ML-Flex: A software package that enables users to integrate with third-party machine-learning packages written in any programming language, execute classification analyses in parallel across multiple computing nodes, and produce HTML reports of classification results.

Data mining Free open-source data mining software and applications

NLTK (Natural Language Toolkit): A suite of libraries and programs for symbolic and statistical natural language processing (NLP) for the Python language.

Data mining Free open-source data mining software and applications

SenticNet API: A semantic and affective resource for opinion mining and sentiment analysis.

Data mining Free open-source data mining software and applications

Orange: A component-based data mining and machine learning software suite written in the Python language.

Data mining Free open-source data mining software and applications

R: A programming language and software environment for statistical computing, data mining, and graphics. It is part of the GNU Project.

Data mining Free open-source data mining software and applications

UIMA: The UIMA (Unstructured Information Management Architecture) is a component framework for analyzing unstructured content such as text, audio and video – originally developed by IBM.

Data mining Free open-source data mining software and applications

Weka: A suite of machine learning software applications written in the Java programming language.

Data mining Commercial data-mining software and applications

Angoss KnowledgeSTUDIO: data mining tool provided by Angoss.

Data mining Commercial data-mining software and applications

BIRT Analytics: visual data mining and predictive analytics tool provided by Actuate Corporation.

Data mining Commercial data-mining software and applications

Clarabridge: enterprise class text analytics solution.

Data mining Commercial data-mining software and applications

IBM DB2 Intelligent Miner: in-database data mining platform provided by IBM, with modeling, scoring and visualization services based on the SQL/MM – PMML framework.

Data mining Commercial data-mining software and applications

LIONsolver: an integrated software application for data mining, business intelligence, and modeling that implements the Learning and Intelligent OptimizatioN (LION) approach.

Data mining Commercial data-mining software and applications

NetOwl: suite of multilingual text and entity analytics products that enable data mining.

Data mining Commercial data-mining software and applications

SAS Enterprise Miner: data mining software provided by the SAS Institute.

Data mining Marketplace surveys

Several researchers and organizations have conducted reviews of data mining tools and surveys of data miners. These identify some of the strengths and weaknesses of the software packages. They also provide an overview of the behaviors, preferences and views of data miners. Some of these reports include:

Data mining Marketplace surveys

Forrester Research 2010 Predictive Analytics and Data Mining Solutions report

Data mining Marketplace surveys

Gartner 2008 “Magic Quadrant” report

Data mining Marketplace surveys

Haughton et al.’s 2003 Review of Data Mining Software Packages in The American Statistician

Data mining Further reading

M.S. Chen, J. Han, P.S. Yu (1996) “Data mining: an overview from a database perspective”. Knowledge and data Engineering, IEEE Transactions on 8 (6), 866-883

Data mining Further reading

Feldman, Ronen; and Sanger, James; The Text Mining Handbook, Cambridge University Press, ISBN 978-0-521-83657-9

Data mining Further reading

Guo, Yike; and Grossman, Robert (editors) (1999); High Performance Data Mining: Scaling Algorithms, Applications and Systems, Kluwer Academic Publishers

Data mining Further reading

Han, Jiawei, Micheline Kamber, and Jian Pei. Data mining: concepts and techniques. Morgan kaufmann, 2006.

Data mining Further reading

Liu, Bing (2007); Web Data Mining: Exploring Hyperlinks, Contents and Usage Data, Springer, ISBN 3-540-37881-2

Data mining Further reading

Murphy, Chris (16 May 2011). “Is Data Mining Free Speech?”. InformationWeek (UMB): 12.

Data mining Further reading

Poncelet, Pascal; Masseglia, Florent; and Teisseire, Maguelonne (editors) (October 2007); “Data Mining Patterns: New Methods and Applications”, Information Science Reference, ISBN 978-1-59904-162-9

Data mining Further reading

Tan, Pang-Ning; Steinbach, Michael; and Kumar, Vipin (2005); Introduction to Data Mining, ISBN 0-321-32136-7

Data mining Further reading

Theodoridis, Sergios; and Koutroumbas, Konstantinos (2009); Pattern Recognition, 4th Edition, Academic Press, ISBN 978-1-59749-272-0

Data mining Further reading

Weiss, Sholom M.; and Indurkhya, Nitin (1998); Predictive Data Mining, Morgan Kaufmann

Data mining Further reading

Witten, Ian H.; Frank, Eibe; Hall, Mark A. (30 January 2011). Data Mining: Practical Machine Learning Tools and Techniques (3 ed.). Elsevier. ISBN 978-0-12-374856-0. (See also Free Weka software)

Data mining Further reading

Ye, Nong (2003); The Handbook of Data Mining, Mahwah, NJ: Lawrence Erlbaum

Data Mining Extensions

Data Mining Extensions (DMX) is a query language for Data Mining Models supported by Microsoft’s SQL Server Analysis Services product.

Data Mining Extensions

DMX is used to create and train data mining models, and to browse, manage, and predict against them

Data Mining Extensions – DMX Queries

DMX Queries are formulated using the SELECT statement. They can extract information from existing data mining models in various ways.

Data Mining Extensions – Data Definition Language

The Data Definition Language (DDL) part of DMX can be used to

Data Mining Extensions – Data Definition Language

Create new data mining models and mining structures – CREATE MINING STRUCTURE, CREATE MINING MODEL

Data Mining Extensions – Data Definition Language

Delete existing data mining models and mining structures – DROP MINING STRUCTURE, DROP MINING MODEL

Data Mining Extensions – Data Definition Language

Export and import mining structures – EXPORT, IMPORT

Data Mining Extensions – Data Manipulation Language

The Data Manipulation Language (DML) part of DMX can be used to

Data Mining Extensions – Data Manipulation Language

Make predictions using mining model – SELECT … FROM PREDICTION JOIN

Data Mining Extensions – Example: a prediction query

This example is a singleton prediction query, which predicts for the given customer whether she will be interested in home loan products.

Data Mining Extensions – Example: a prediction query

NATURAL PREDICTION JOIN

Data Mining Extensions – Example: a prediction query

18 AS [Total Years of Education]

OAuth – Abuse of OAuth for Internet data mining

A growing number of social networking services promote OAuth logins to the dominant social networks (Facebook, Twitter, etc.) as the primary authentication method, over “traditional” email confirmation type processes

OAuth – Abuse of OAuth for Internet data mining

The use of OAuth logins to social networks for “authentication” permits the application provider to legitimately circumvent the often significant restrictions on API use put in place by social network providers to prevent large-scale data extraction

Social networking service – Data mining

Through data mining, companies are able to improve their sales and profitability

United States Department of Homeland Security – Data mining (ADVISE)

The Associated Press reported on September 5, 2007, that DHS had scrapped an anti-terrorism data mining tool called ADVISE (Analysis, Dissemination, Visualization, Insight and Semantic Enhancement) after the agency’s Privacy Office and Office of Inspector General (OIG) found that pilot testing of the system had been performed using data on real people without having done a Privacy Impact Assessment, a required privacy safeguard for the various uses of real personally identifiable information required by section 208 of the e-Government Act of 2002

Multitenancy – Data aggregation/data mining

One of the most compelling reasons for vendors/ISVs to utilize multitenancy is for the inherent data aggregation benefits

Machine learning – Machine learning and data mining

These two terms are commonly confused, as they often employ the same methods and overlap significantly. They can be roughly defined as follows:

Machine learning – Machine learning and data mining

Machine learning focuses on prediction, based on known properties learned from the training data.

Machine learning – Machine learning and data mining

Data mining focuses on the discovery of (previously) unknown properties in the data. This is the analysis step of Knowledge Discovery in Databases.

Machine learning – Machine learning and data mining

Much of the confusion between these two research communities (which do often have separate conferences and separate journals, ECML PKDD being a major exception) comes from the basic assumptions they work with: in machine learning, performance is usually evaluated with respect to the ability to reproduce known knowledge, while in Knowledge Discovery and Data Mining (KDD) the key task is the discovery of previously unknown knowledge

Surveillance – Data mining and profiling

Data mining is the application of statistical techniques and programmatic algorithms to discover previously unnoticed relationships within the data.

Surveillance – Data mining and profiling

Economic (such as Creditcard purchases) and social (such as telephone calls and emails) transactions in modern society create large amounts of stored data and records. In the past, this data was documented in paper records, leaving a paper trail, or was simply not documented at all. Correlation of paper-based records was a laborious process—it required human intelligence operators to manually dig through documents, which was time-consuming and incomplete, at best.

Surveillance – Data mining and profiling

But today many of these records are electronic, resulting in an electronic trail

Surveillance – Data mining and profiling

Information relating to many of these individual transactions is often easily available because it is generally not guarded in isolation, since the information, such as the title of a movie a person has rented, might not seem sensitive

Surveillance – Data mining and profiling

In addition to its own aggregation and profiling tools, the government is able to access information from third parties— for example, banks, credit companies or employers, etc.— by requesting access informally, by compelling access through the use of subpoenas or other procedures, or by purchasing data from commercial data aggregators or data brokers

Surveillance – Data mining and profiling

Under [http://caselaw.lp.findlaw.com/scripts/getcase.pl?court=usvol=425invol=435 United States v. Miller] (1976), data held by third parties is generally not subject to Fourth Amendment to the United States Constitution|Fourth Amendment warrant requirements.

Criticism of Facebook – Data mining

There have been some concerns expressed regarding the use of Facebook as a means of surveillance and data mining

Criticism of Facebook – Data mining

The possibility of data mining by private individuals unaffiliated with Facebook has been a concern, as evidenced by the fact that two Massachusetts Institute of Technology (MIT) students were able to download, using an automated script, over 70,000 Facebook profiles from four schools (MIT, NYU, the University of Oklahoma, and Harvard University) as part of a research project on Facebook privacy published on December 14, 2005

Criticism of Facebook – Data mining

A second clause that brought criticism from some users allowed Facebook the right to sell users’ data to private companies, stating We may share your information with third parties, including responsible companies with which we have a relationship. This concern was addressed by spokesman Chris Hughes, who said Simply put, we have never provided our users’ information to third party companies, nor do we intend to. Facebook eventually removed this clause from its privacy policy.

Criticism of Facebook – Data mining

Previously, third party applications had access to almost all user information. Facebook’s privacy policy previously stated: Facebook does not screen or approve Platform Developers and cannot control how such Platform Developers use any personal information. However, that language has since been removed. Regarding use of user data by third party applications, the ‘Preapproved Third-Party Websites and Applications’ section of the Facebook privacy policy now states:

Criticism of Facebook – Data mining

In the United Kingdom, the Trades Union Congress (TUC) has encouraged employers to allow their staff to access Facebook and other social-networking sites from work, provided they proceed with caution.

Criticism of Facebook – Data mining

In September 2007, Facebook drew a fresh round of criticism after it began allowing non-members to search for users, with the intent of opening limited public profiles up to search engines such as Google in the following months. Facebook’s privacy settings, however, allow users to block their profiles from search engines.

Criticism of Facebook – Data mining

Concerns were also raised on the Watchdog (TV series)|BBC’s Watchdog program in October 2007 when Facebook was shown to be an easy way in which to collect an individual’s personal information in order to facilitate identity theft. However, there is barely any personal information presented to non-friends – if users leave the privacy controls on their default settings, the only personal information visible to a non-friend is the user’s name, gender, profile picture, networks, and user name.

Criticism of Facebook – Data mining

A New York Times article in February 2008 pointed out that Facebook does not actually provide a mechanism for users to close their accounts, and raised the concern that private user data would remain indefinitely on Facebook’s servers. , Facebook gives users the options to deactivate or delete their accounts.

Criticism of Facebook – Data mining

Deactivating an account allows it to be restored later, while deleting it will remove the account permanently, although some data submitted by that account (like posting to a group or sending someone a message) will remain.

Criticism of Facebook – Data mining

A third party site, uSocial, was involved in a controversy surrounding the sale of fans and friends. uSocial received a cease-and-desist letter from Facebook and has stopped selling friends.

Data visualization – Data mining

Data mining is the process of sorting through large amounts of data and picking out relevant information. It is usually used by business intelligence organizations, and financial analysts, but is increasingly being used in the sciences to extract information from the enormous data sets generated by modern experimental and observational methods.

Data visualization – Data mining

It has been described as the nontrivial extraction of implicit, previously unknown, and potentially useful information from data and the science of extracting useful information from large data sets or databases. In relation to enterprise resource planning, according to Monk (2006), data mining is the statistical and logical analysis of large sets of transaction data, looking for patterns that can aid decision making.

Mass surveillance in the United States – Data mining of subpoenaed records

The Federal Bureau of Investigation|FBI collected nearly all hotel, airline, rental car, gift shop, and casino records in Las Vegas, Nevada|Las Vegas during the last two weeks of 2003

Oracle Data Mining

It provides means for the creation, management and operational deployment of data mining models inside the database environment.

Oracle Data Mining – Overview

These operations include functions to Data Definition Language|create, apply, Test method|test, and Data manipulation|manipulate data mining models

Oracle Data Mining – Overview

In data mining, the process of using a model to derive predictions or descriptions of behavior that is yet to occur is called scoring

Oracle Data Mining – Overview

Most Oracle Data Mining functions also allow text mining by accepting Text (unstructured data) attributes as input

Oracle Data Mining – History

Oracle Data Mining was first introduced in 2002 and its releases are named according to the corresponding Oracle database release:

Oracle Data Mining – History

* Oracle Data Mining 10gR1 (10.1.0.2.0 – February 2004)

Oracle Data Mining – History

* Oracle Data Mining 10gR2 (10.2.0.1.0 – July 2005)

Oracle Data Mining – History

Oracle Data Mining is a logical successor of the Darwin data mining toolset developed by Thinking Machines Corporation in the mid-1990s and later distributed by Oracle after its acquisition of Thinking Machines in 1999. However, the product itself

Oracle Data Mining – History

is a Rewrite (programming)|complete redesign and rewrite from ground-up – while Darwin was a classic GUI-based analytical workbench, ODM offers a data mining development/deployment platform integrated into the Oracle database, along with the Oracle Data Miner GUI.

Oracle Data Mining – History

The Oracle Data Miner 11gR2 New Workflow GUI was previewed at Oracle Open World 2009. An updated Oracle Data Miner GUI was released in 2012. It is free, and is available as an extension to Oracle SQL Developer 3.1 .

Oracle Data Mining – Functionality

As of release 11gR1 Oracle Data Mining contains the following data mining functions:

Oracle Data Mining – Functionality

** Model exploration, evaluation and analysis.

Oracle Data Mining – Functionality

* Feature selection (Attribute Importance).

Oracle Data Mining – Functionality

** Support Vector Machine (SVM).

Oracle Data Mining – Functionality

** One-class Support Vector Machine (SVM).

Oracle Data Mining – Functionality

** Generalized linear model (GLM) for Multiple regression

Oracle Data Mining – Functionality

** Orthogonal Partitioning Clustering (O-Cluster).

Oracle Data Mining – Functionality

* Association rule learning:

Oracle Data Mining – Functionality

** Itemsets and association rules (AM).

Oracle Data Mining – Functionality

* Feature extraction.

Oracle Data Mining – Functionality

** Combined text and non-text columns of input data.

Oracle Data Mining – Input sources and data preparation

Most Oracle Data Mining functions accept as input one relational table or view. Flat data can be combined with transactional data through the use of nested columns, enabling mining of data involving one-to-many relationships (e.g. a star schema). The full functionality of SQL can be used when preparing data for data mining, including dates and spatial data.

Oracle Data Mining – Input sources and data preparation

Oracle Data Mining distinguishes numerical, categorical, and unstructured (text) attributes. The product also provides utilities for data preparation steps prior to model building such as outlier treatment, discretization, Database normalization|normalization and binning (sorting in general speak)

Oracle Data Mining – Graphical user interface: Oracle Data Miner

There is also an independent interface: the Spreadsheet Add-In for Predictive Analytics which enables access to the Oracle Data Mining Predictive Analytics PL/SQL package from Microsoft Excel.

Oracle Data Mining – PL/SQL and Java interfaces

Oracle Data Mining provides a native PL/SQL package (DBMS_DATA_MINING) to create, destroy, describe, apply, test, export and import models. The code below illustrates a typical call to build a Statistical classification|classification model:

Oracle Data Mining – PMML

In Release 11gR2 (11.2.0.2), ODM supports the import of externally-created PMML for some of the data mining models. PMML is an XML-based standard for representing data mining models.

Oracle Data Mining – Predictive Analytics MS Excel Add-In

The PL/SQL package DBMS_PREDICTIVE_ANALYTICS automates the data mining process including data preprocessing, model building and evaluation, and scoring of new data

Oracle Data Mining – References and further reading

* T. H. Davenport, [ http://www.lbl.gov/BLI/BLI_Library/assets/articles/OM/OM_PSDM_Competing_Analytics.pdf Competing on Analytics], Harvard Business Review, January 2006.

Oracle Data Mining – References and further reading

* I. Ben-Gal,[ http://www.eng.tau.ac.il/~bengal/outlier.pdf Outlier detection], In: Maimon O. and Rockach L. (Eds.) Data Mining and Knowledge Discovery Handbook: A Complete Guide for Practitioners and Researchers, Kluwer Academic Publishers, 2005, ISBN 0-387-24435-2.

Oracle Data Mining – References and further reading

* M. M. Campos, P. J. Stengard, and B. L. Milenova, Data-centric Automated Data Mining. In proceedings of the Fourth International Conference on Machine Learning and Applications 2005, 15–17 December 2005. pp8, ISBN 0-7695-2495-8

Oracle Data Mining – References and further reading

* M. F. Hornick, Erik Marcade, and Sunil Venkayala. Java Data Mining: Strategy, Standard, and Practice. Morgan-Kaufmann, 2006, ISBN 0-12-370452-9.

Oracle Data Mining – References and further reading

* B. L. Milenova, J. S. Yarmus, and M. M. Campos. SVM in Oracle database 10g: removing the barriers to widespread adoption of support vector machines. In Proceedings of the 31st international Conference on Very Large Data Bases (Trondheim, Norway, August 30 – September 2, 2005). pp1152–1163, ISBN 1-59593-154-6.

Oracle Data Mining – References and further reading

* B. L. Milenova and M. M. Campos. O-Cluster: scalable clustering of large high dimensional data sets. In proceedings of the 2002 IEEE International Conference on Data Mining: ICDM 2002. pp290–297, ISBN 0-7695-1754-4.

Oracle Data Mining – References and further reading

* P. Tamayo, C. Berger, M. M. Campos, J. S. Yarmus, B. L.Milenova, A. Mozes, M. Taft, M. Hornick, R. Krishnan, S.Thomas, M. Kelly, D. Mukhin, R. Haberstroh, S. Stephens and J. Myczkowski. Oracle Data Mining – Data Mining in the Database Environment. In Part VII of Data Mining and Knowledge Discovery Handbook, Maimon, O.; Rokach, L. (Eds.) 2005, p315-1329, ISBN 0-387-24435-2.

Oracle Data Mining – References and further reading

* Brendan Tierney, Predictive Analytics using Oracle Data Miner: for the data scientist, oracle analyst, oracle developer DBA, Oracle Press, McGraw Hill, Spring 2014.

Computational sociology – Data mining and social network analysis

Independent from developments in computational models of social systems, social network analysis emerged in the 1970s and 1980s from advances in graph theory, statistics, and studies of social structure as a distinct analytical method and was articulated and employed by sociologists like James Samuel Coleman|James S

Department of Homeland Security – Data mining (ADVISE)

found that Pilot (experiment)|pilot testing of the system had been performed using data on real people without having done a Privacy Impact Assessment, a required privacy safeguard for the various uses of real personally identifiable information required by section 208 of the e-Government Act of 2002

List of free and open-source software packages – Data mining

* Environment for DeveLoping KDD-Applications Supported by Index-Structures|Environment for DeveLoping KDD-Applications Supported by Index-Structures (ELKI) — data mining software framework written in Java with a focus on clustering and outlier detection methods.

List of free and open-source software packages – Data mining

* Orange (software) — data visualization and data mining for novice and experts, through visual programming or Python scripting. Extensions for bioinformatics and text mining.

List of free and open-source software packages – Data mining

* RapidMiner — data mining software written in Java, fully integrating Weka, featuring 350+ operators for preprocessing, machine learning, visualization, etc.

List of free and open-source software packages – Data mining

* Scriptella|Scriptella ETL — Extract transform load|ETL (Extract-Transform-Load) and script execution tool. Supports integration with J2EE and Spring. Provides connectors to CSV, LDAP, XML, JDBC/ODBC and other data sources.

List of free and open-source software packages – Data mining

* Weka (machine learning)|Weka — data mining software written in Java featuring machine learning operators for classification, regression, and clustering.

List of open-source software packages – Data mining

* OpenNN — Open source neural networks software library written in the C++ programming language.

Learning analytics – Differentiating Learning Analytics and Educational Data Mining

They go on to attempt to disambiguate educational data mining from academic analytics based on whether the process is hypothesis driven or not, though Brooks C

Learning analytics – Differentiating Learning Analytics and Educational Data Mining

Regardless of the differences between the LA and EDM communities, the two areas have significant overlap both in the objectives of investigators as well as in the methods and techniques that are used in the investigation.

Customer analytics – Data mining

There are two types of categories of data mining. Predictive models use previous customer interactions to predict future events while segmentation techniques are used to place customers with similar behaviors and attributes into distinct groups. This grouping can help marketers to optimize their campaign management and targeting processes.

Conference on Knowledge Discovery and Data Mining

‘SIGKDD’ is the Association for Computing Machinery’s Association for Computing Machinery#Special Interest Groups|Special Interest Group on Knowledge Discovery and Data Mining. It became an official ACM SIG in 1998. The official web page of SIGKDD can be found on www.KDD.org.

Conference on Knowledge Discovery and Data Mining – Conferences

SIGKDD has hosted an annual conference – ‘ACM SIGKDD Conference on Knowledge Discovery and Data Mining’ (‘KDD’) – since 1995. KDD Conferences grew from KDD (Knowledge Discovery and Data Mining) workshops at AAAI conferences, which were started by Wikipedia:Gregory I. Piatetsky-Shapiro|Gregory Piatetsky-Shapiro in 1989, 1991, and 1993, and Usama Fayyad in 1994.

Conference on Knowledge Discovery and Data Mining – Conferences

http://www.sigkdd.org/conferences.php Conference papers of each Proceedings of the SIGKDD International Conference on Knowledge Discovery and Data Mining are published through Association for Computing Machinery|ACMhttp://dl.acm.org/event.cfm?id=RE329

Conference on Knowledge Discovery and Data Mining – Conferences

KDD-2012 took place in Beijing, China,http://kdd2012.sigkdd.org/ KDD-2013 took place in Chicago, USA., and KDD-2014 will take place in New York City, USA., August 24–27, 2014. Here is a full list of past KDD meetings.http://www.kdnuggets.com/meetings/past-meetings-kdd.html

Conference on Knowledge Discovery and Data Mining – KDD-Cup

SIGKDD sponsors the [http://www.kdd.org/kddcup/ KDD Cup] competition every year in conjunction with the annual conference. It is aimed at members of the industry and academia, particularly students, interested in KDD.

Conference on Knowledge Discovery and Data Mining – Awards

The group also annually recognizes members of the KDD community with its [http://www.kdd.org/sigkdd-innovation-award Innovation Award] and [http://www.kdd.org/innovation-service-awards Service Award]. Additionally, KDD presents a Best Paper Award to recognize the highest quality paper at each conference.

Conference on Knowledge Discovery and Data Mining – SIGKDD Explorations

SIGKDD has also published a biannual academic journal titled [http://www.kdd.org/explorations/ SIGKDD Explorations] since June, 1999.

Conference on Knowledge Discovery and Data Mining – Leadership

The new SIGKDD leadership team took office on July 1, 2013

Conference on Knowledge Discovery and Data Mining – Leadership

* Wikipedia:Gregory I. Piatetsky-Shapiro|Gregory Piatetsky-Shapirohttp://www.kdnuggets.com/gps.html (2005-2008)

Conference on Knowledge Discovery and Data Mining – Leadership

* David D. Jensenhttp://kdl.cs.umass.edu/people/jensen/

Conference on Knowledge Discovery and Data Mining – Information Directors

* [http://faculty.washington.edu/ankurt/ Ankur Teredesai] (2011-)

Quantitative structure–activity relationship – Data mining approach

Computer SAR models typically calculate a relatively large number of features. Because those lack structural interpretation ability, the preprocessing steps face a feature selection problem (i.e., which structural features should be interpreted to determine the structure-activity relationship). Feature selection can be accomplished by visual inspection (qualitative selection by a human); by data mining; or by molecule mining.

Quantitative structure–activity relationship – Data mining approach

A typical data mining based prediction uses e.g. support vector machines, decision trees, neural networks for inductive reasoning|inducing a predictive learning model.

Quantitative structure–activity relationship – Data mining approach

Molecule mining approaches, a special case of structured data mining approaches, apply a similarity matrix based prediction or an automatic fragmentation scheme into molecular substructures. Furthermore there exist also approaches using Maximum common subgraph isomorphism problem|maximum common subgraph searches or graph kernels.

Data mining in meteorology

Meteorology is the interdisciplinary scientific study of the atmosphere. It observes the changes in temperature, air pressure, moisture and wind direction. Usually, temperature, pressure, wind measurements and humidity are the variables that are measured by a thermometer, barometer, anemometer, and hygrometer, respectively. There are many methods of collecting data and Radar, Lidar, satellites are some of them.

Data mining in meteorology

Weather forecasts are made by collecting quantitative data about the current state of the atmosphere. The main issue arise in this prediction is, it involves high-dimensional characters. To overcome this issue, it is necessary to first analyze and simplify the data before proceeding with other analysis. Some data mining techniques are appropriate in this context.

Data mining in meteorology – What is Data mining?

Consequently, data mining consists of more than collecting and analyzing data, it also includes analyze and predictions

Data mining in meteorology – What is Data mining?

The network architecture and signal process used to model nervous systems can roughly be divided into three categories, each based on a different philosophy.

Data mining in meteorology – What is Data mining?

#Feedforward neural network: the input information defines the initial signals into set of output signals.

Data mining in meteorology – What is Data mining?

#Feedback network: the input information defines the initial activity state of a feedback system, and after state transitions, the asymptotic final state is identified as the outcome of the computation.

Data mining in meteorology – What is Data mining?

#Neighboring cells in a neural network compete in their activities by means of mutual lateral interactions, and develop adaptively into specific detectors of different signal patterns. In this category, learning is called competitive, unsupervised learning or self-organizing.

Data mining in meteorology – Self-organizing Maps

Self-Organizing Map (SOM) is one of the most popular neural network models, which is especially suitable for high dimensional data visualization, clustering and modeling

Data mining in meteorology – Self-organizing Maps

The Self-Organizing Map projects high-dimensional input data onto a low dimensional (usually two-dimensional) space

Data mining in meteorology – Self-organizing Maps

According to the first input of the input vector, System chooses the output neuron (winning neuron) that closely matches with the given input vector

Police-enforced ANPR in the UK – Data mining

A major feature of the National ANPR Data Centre for car numbers is the ability to data mining|data mine. Advanced versatile automated data mining software trawls through the vast amounts of data collected, finding patterns and meaning in the data. Data mining can be used on the records of previous sightings to build up intelligence of a vehicle’s movements on the road network or can be used to find cloned vehicles by searching the database for impossibly quick journeys.

Police-enforced ANPR in the UK – Data mining

We can use ANPR on investigations or we can use it looking forward in a proactive, intelligence way

Multifactor dimensionality reduction – Data mining with MDR

Another approach is to generate many random permutations of the data to see what the data mining algorithm finds when given the chance to overfit

Educational data mining

Baker (2010) Data Mining for Education

Educational data mining – Definition

Educational Data Mining refers to techniques, tools, and research designed for automatically extracting meaning from large repositories of data generated by or related to people’s learning activities in educational settings

Educational data mining – Definition

In other cases, the data is less fine-grained

Educational data mining – History

Educational Data Mining: A Review of the State-of-the-Art

Educational data mining – History

As interest in EDM continued to increase, EDM researchers established an academic journal in 2009, the [http://www.educationaldatamining.org/JEDM/ Journal of Educational Data Mining], for sharing and disseminating research results. In 2011, EDM researchers established the [http://educationaldatamining.org/ International Educational Data Mining Society] to connect EDM researchers and continue to grow the field.

Educational data mining – History

With the introduction of public educational data repositories in 2008, such as the Pittsburgh Science of Learning Centre’s (PSLC) DataShop and the National Center for Education Statistics (NCES), public data sets have made educational data mining more accessible and feasible, contributing to its growth.

Educational data mining – Goals

Baker and Yacef identified the following four goals of EDM:

Educational data mining – Goals

#’Predicting students’ future learning behavior’ – With the use of student modeling, this goal can be achieved by creating student models that incorporate the learner’s characteristics, including detailed information such as their knowledge, behaviours and motivation to learn. The user experience of the learner and their overall Contentment|satisfaction with learning are also measured.

Educational data mining – Goals

#’Discovering or improving domain models’ – Through the various methods and applications of EDM, discovery of new and improvements to existing models is possible. Examples include illustrating the educational content to engage learners and determining optimal instructional sequences to support the student’s learning style.

Educational data mining – Goals

#’Studying the effects of educational support’ that can be achieved through learning systems.

Educational data mining – Goals

#’Advancing scientific knowledge about learning and learners’ by building and incorporating student models, the field of EDM research and the technology and software used.

Educational data mining – Users and Stakeholders

There are four main users and stakeholders involved with educational data mining. These include:

Educational data mining – Users and Stakeholders

JEDM-Journal of Educational Data Mining 5.2 (2013): 102-126.

Educational data mining – Users and Stakeholders

* ‘Educators’ – Educators attempt to understand the learning process and the methods they can use to improve their teaching methods

Educational data mining – Users and Stakeholders

* ‘Researchers’ – Researchers focus on the development and the evaluation of data mining techniques for effectiveness. A yearly international conference for researchers began in 2008, followed by the establishment of the [http://www.educationaldatamining.org/JEDM/index.php/JEDM Journal of Educational Data Mining] in 2009. The wide range of topics in EDM ranges from using data mining to improve institutional effectiveness to student performance.

Educational data mining – Users and Stakeholders

* ‘Administrator (business)|Administrators’ – Administrators are responsible for allocating the resources for implementation in institutions

Educational data mining – Phases of Educational Data Mining

As research in the field of educational data mining has continued to grow, a myriad of data mining techniques have been applied to a variety of educational contexts. In each case, the goal is to translate raw data into meaningful information about the learning process in order to make better decisions about the design and trajectory of a learning environment. Thus, EDM generally consists of four phases:

Educational data mining – Phases of Educational Data Mining

# The first phase of the EDM process (not counting pre-processing) is discovering relationships in data

Educational data mining – Phases of Educational Data Mining

# Discovered relationships must then be Validity (statistics)|validated in order to avoid overfitting.

Educational data mining – Phases of Educational Data Mining

# Validated relationships are applied to make predictions about future events in the learning environment.

Educational data mining – Phases of Educational Data Mining

# Predictions are used to support decision-making processes and policy decisions.

Educational data mining – Phases of Educational Data Mining

During phases 3 and 4, data is often visualized or in some other way distilled for human judgment. A large amount of research has been conducted in best practices for Data visualization|visualizing data.

Educational data mining – Main Approaches

Of the general categories of methods mentioned, prediction, Cluster analysis|clustering and relationship mining are considered universal methods across all types of data mining; however, ‘Discovery with Models’ and ‘Distillation of Data for Human Judgment’ are considered more prominent approaches within educational data mining.

Educational data mining – Discovery with Models

In the Discovery with Model method, a model is developed via prediction, clustering or by human reasoning knowledge engineering and then used as a component in another analysis, namely in prediction and relationship mining

Educational data mining – Discovery with Models

Key applications of this method include discovering relationships between student behaviors, characteristics and contextual variables in the learning environment. Further discovery of broad and specific research questions across a wide range of contexts can also be explored using this method.

Educational data mining – Distillation of Data for Human Judgment

Humans can make inferences about data that may be beyond the scope in which an automated data mining method provides. For the use of education data mining, data is distilled for human judgment for two key purposes, Identification (information)|identification and Statistical classification|classification.

Educational data mining – Distillation of Data for Human Judgment

For the purpose of Identification (information)|identification, data is distilled to enable humans to identify well-known patterns, which may otherwise be difficult to interpret. For example, the learning curve, classic to educational studies, is a pattern that clearly reflects the relationship between learning and experience over time.

Educational data mining – Distillation of Data for Human Judgment

Data is also distilled for the purposes of Statistical classification|classifying features of data, which for educational data mining, is used to support the development of the prediction model. Classification helps expedite the development of the prediction model, tremendously.

Educational data mining – Distillation of Data for Human Judgment

The goal of this method is to summarize and present the information in a useful, interactive and visually appealing way in order to understand the large amounts of education data and to support decision making

Educational data mining – Applications

A list of the primary applications of EDM is provided by Cristobal Romero and Sebastian Ventura. In their taxonomy, the areas of EDM application are:

Educational data mining – Applications

* Providing feedback for supporting instructors

Educational data mining – Applications

* Recommendations for students

Educational data mining – Applications

* Predicting student performance

Educational data mining – Applications

* Detecting undesirable student behaviors

Educational data mining – Applications

* Constructing courseware – EDM can be applied to course management systems such as open source Moodle. Moodle contains usage data that includes various activities by users such as test results, amount of readings completed and participation in discussion forums. Data mining tools can be used to customize learning activities for each user and adapt the pace in which the student completes the course. This is in particularly beneficial for online courses with varying levels of competency.

Educational data mining – Applications

New research on Mobile phone|mobile learning environments also suggests that data mining can be useful. Data mining can be used to help provide personalized content to mobile users, despite the differences in managing content between mobile devices and standard Personal computer|PCs and web browsers.

Educational data mining – Applications

New EDM applications will focus on allowing non-technical users use and engage in data mining tools and activities, making data collection and processing more accessible for all users of EDM. Examples include statistical and visualization tools that analyzes social networks and their influence on learning outcomes and productivity.

Educational data mining – Courses

In October 2013, Coursera offered a free online course on “Big Data in Education” that teaches how and when to use key methods for EDM. A course archive is now available online.

Educational data mining – Courses

Teachers College, Columbia University offers a Learning Analytics focus as part of its Cognitive Studies Masters. http://catalog.tc.columbia.edu/tc/departments/humandevelopment/cognitivestudiesineducation/

Educational data mining – Publication Venues

Considerable amounts of EDM work are published at the peer-reviewed International Conference on Educational Data Mining, organized by the [http://www.educationaldatamining.org/ International Educational Data Mining Society].

Educational data mining – Publication Venues

* [http://www.educationaldatamining.org/EDM2008 1st International Conference on Educational Data Mining] (2008) — Montreal, Canada

Educational data mining – Publication Venues

* [http://www.educationaldatamining.org/EDM2009 2nd International Conference on Educational Data Mining] (2009) — Cordoba, Spain

Educational data mining – Publication Venues

* [http://www.educationaldatamining.org/EDM2010 3rd International Conference on Educational Data Mining] (2010) — Pittsburgh, USA

Educational data mining – Publication Venues

* [http://www.educationaldatamining.org/EDM2011 4th International Conference on Educational Data Mining] (2011) — Eindhoven, Netherlands

Educational data mining – Publication Venues

* [http://www.educationaldatamining.org/EDM2012 5th International Conference on Educational Data Mining] (2012) — Chania, Greece

Educational data mining – Publication Venues

* [http://www.educationaldatamining.org/EDM2013 6th International Conference on Educational Data Mining] (2013) — Memphis, USA

Educational data mining – Publication Venues

EDM papers are also published in the [http://www.educationaldatamining.org/JEDM/ Journal of Educational Data Mining] (JEDM).

Educational data mining – Publication Venues

Many EDM papers are routinely published in related conferences, such as Artificial Intelligence and Education, Intelligent Tutoring Systems, and User Modeling and Adaptive Personalization.

Educational data mining – Publication Venues

In 2011, Chapman Hall/CRC Press, Taylor and Francis Group published the first Handbook of Educational Data Mining. This resource was created for those that are interested in participating in the educational data mining community.

Educational data mining – Contests

In 2010, the Association for Computing Machinery’s [http://www.kdd.org/kdd2010/kddcup.shtml KDD Cup] was conducted using data from an educational setting

Educational data mining – Costs and Challenges

Along with technological advancements are costs and challenges associated with implementing EDM applications

Educational data mining – Criticisms

Research also indicates that the field of educational data mining is concentrated in North America and western cultures and subsequently, other countries and cultures may not be represented in the research and findings

Educational data mining – Criticisms

As users become savvy in their understanding of online privacy, Business Administrator|administrators of educational data mining tools need to be proactive in protecting the privacy of their users and be transparent about how and with whom the information will be used and shared

Educational data mining – Criticisms

* ‘Plagiarism’ – Plagiarism detection is an ongoing challenge for educators and faculty whether in the classroom or online. However, due to the complexities associated with detecting and preventing digital plagiarism in particular, educational data mining tools are not currently sophisticated enough to accurately address this issue. Thus, the development of predictive capability in plagiarism-related issues should be an area of focus in future research.

Educational data mining – Criticisms

* ‘Adoption’ – It is unknown how widespread the adoption of EDM is and the extent to which institutions have applied and considered implementing an EDM strategy. As such, it is unclear whether there are any barriers that prevent users from adopting EDM in their educational settings.

Java Data Mining

JDM enables applications to integrate data mining technology for developing predictive analytics applications and tools

Java Data Mining

Various data mining functions and techniques like statistical classification and association (statistics)|association, regression analysis, data clustering, and attribute importance are covered by the 1.0 release of this standard.

Cross Industry Standard Process for Data Mining

In Proceedings of the IADIS European Conference on Data Mining 2008, pp 182-185.

Cross Industry Standard Process for Data Mining – Major phases

The lessons learned during the process can trigger new, often more focused business questions and subsequent data mining processes will benefit from the experiences of previous ones.

Cross Industry Standard Process for Data Mining – Major phases

;Business Understanding: This initial phase focuses on understanding the project objectives and requirements from a business perspective, and then converting this knowledge into a data mining problem definition, and a preliminary plan designed to achieve the objectives.

Cross Industry Standard Process for Data Mining – Major phases

;Data Understanding: The data understanding phase starts with an initial data collection and proceeds with activities in order to get familiar with the data, to identify data quality problems, to discover first insights into the data, or to detect interesting subsets to form hypotheses for hidden information.

Cross Industry Standard Process for Data Mining – Major phases

;Data Preparation: The data preparation phase covers all activities to construct the final dataset (data that will be fed into the modeling tool(s)) from the initial raw data. Data preparation tasks are likely to be performed multiple times, and not in any prescribed order. Tasks include table, record, and attribute selection as well as transformation and cleaning of data for modeling tools.

Cross Industry Standard Process for Data Mining – Major phases

;Modeling: In this phase, various modeling techniques are selected and applied, and their parameters are calibrated to optimal values. Typically, there are several techniques for the same data mining problem type. Some techniques have specific requirements on the form of data. Therefore, stepping back to the data preparation phase is often needed.

Cross Industry Standard Process for Data Mining – Major phases

At the end of this phase, a decision on the use of the data mining results should be reached.

Cross Industry Standard Process for Data Mining – Major phases

Depending on the requirements, the deployment phase can be as simple as generating a report or as complex as implementing a repeatable data mining process

Cross Industry Standard Process for Data Mining – History

CRISP-DM was conceived in 1996. In 1997 it got underway as a European Union project under the European Strategic Program on Research in Information Technology|ESPRIT funding initiative. The project was led by five companies: SPSS Inc.|SPSS, Teradata, Daimler AG, NCR Corporation and OHRA, an insurance company.

Cross Industry Standard Process for Data Mining – History

This core consortium brought different experiences to the project: ISL, later acquired and merged into SPSS Inc. The computer giant NCR Corporation produced the Teradata data warehouse and its own data mining software. Daimler-Benz had a significant data mining team. OHRA was just starting to explore the potential use of data mining.

Cross Industry Standard Process for Data Mining – History

and published as a step-by-step data mining guide later that year.Pete Chapman, Julian Clinton, Randy Kerber, Thomas Khabaza, Thomas Reinartz, Colin Shearer, and Rüdiger Wirth (2000); [ftp://ftp.software.ibm.com/software/analytics/spss/support/Modeler/Documentation/14/UserManual/CRISP-DM.pdf CRISP-DM 1.0 Step-by-step data mining guides].

Cross Industry Standard Process for Data Mining – History

Between 2006 and 2008 a CRISP-DM 2.0 SIG was formed and there were discussions about updating the CRISP-DM process model.Colin Shearer (2006); [http://www.kdnuggets.com/news/2006/n19/4i.html First CRISP-DM 2.0 Workshop Held] The current status of these efforts is not known. However, the original crisp-dm.org website cited in the reviews, and the CRISP-DM 2.0 SIG website are both no longer active.

Cross Industry Standard Process for Data Mining – History

While many non-IBM data mining practitioners use CRISP-DM, IBM is the primary corporation that currently embraces the CRISP-DM process model. It makes some of the old CRISP-DM documents available for download and it has incorporated it into its SPSS Modeler product.

Data mining in agriculture

‘Data mining in agriculture’ is a very recent research topic. It consists in the application of data mining techniques to agriculture. Recent technologies are nowadays able to provide a lot of information on agricultural-related activities, which can then be analyzed in order to find important information. A related, but not equivalent term is precision agriculture.

Data mining in agriculture – Prediction of problematic wine fermentations

Wine is widely produced all around the world

Data mining in agriculture – Detection of diseases from sounds issued by animals

The detection of animal’s diseases in farms can impact positively the productivity of the farm, because sick animals can cause contaminations

Data mining in agriculture – Sorting apples by watercores

For this reason, a computational system is under study which takes X-ray photographs of the fruit while they run on conveyor belts, and which is also able to analyse (by data mining techniques) the taken pictures and estimate the probability that the fruit contains watercores.

Data mining in agriculture – Optimizing pesticide use by data mining

By data mining the cotton Pest Scouting data along with the meteorological recordings it was shown that how pesticide use can be optimized (reduced)

Data mining in agriculture – Explaining pesticide abuse by data mining

Creating a novel Pilot Agriculture Extension Data Warehouse followed by analysis through querying and data mining some interesting discoveries were made, such as pesticides sprayed at the wrong time, wrong pesticides used for the right reasons and temporal relationship between pesticide usage and day of the week.

Data mining in agriculture – Literature

There are a few precision agriculture journals, such as Springer’s [http://www.springerlink.com/content/103317/ Precision Agriculture] or Elsevier’s [http://www.sciencedirect.com/science/journal/01681699 Computers and Electronics in Agriculture], but those are not exclusively devoted to data mining in agriculture.

Data mining in agriculture – Conferences

There are many conferences organized every year on data mining techniques and applications, but rather few of them consider problems arising in the agricultural field. To date, there is only one example of a conference completely devoted to applications in agriculture of data mining. It is organized by Georg Ruß. This is the conference [http://dma-workshop.de/ web page].

Dependent variables – Data mining

In data mining tools (for multivariate statistics and machine learning), the depending variable is assigned a role as ‘target variable’ (or in some tools as label attribute), while a dependent variable may be assigned a role as regular variable.[http://1xltkxylmzx3z8gd647akcdvov.wpengine.netdna-cdn.com/wp-content/uploads/2013/10/rapidminer-5.0-manual-english_v1.0.pdf English Manual version 1.0] for RapidMiner 5.0, October 2013

Learning algorithms – Machine learning and data mining

* Machine learning focuses on prediction, based on known properties learned from the training data.

Learning algorithms – Machine learning and data mining

* Data mining focuses on the discovery (observation)|discovery of (previously) unknown properties in the data. This is the analysis step of Knowledge discovery|Knowledge Discovery in Databases.

Learning algorithms – Machine learning and data mining

Much of the confusion between these two research communities (which do often have separate conferences and separate journals, ECML PKDD being a major exception) comes from the basic assumptions they work with: in machine learning, performance is usually evaluated with respect to the ability to reproduce known knowledge, while in Knowledge Discovery and Data Mining (KDD) the key task is the discovery of previously unknown knowledge

Activity recognition – Data mining based approach to activity recognition

They proposed a data mining approach based on discriminative patterns which describe significant changes between any two activity classes of data to recognize sequential, interleaved and concurrent activities in a unified solution.

Activity recognition – Data mining based approach to activity recognition

Gilbert et al.Gilbert A, Illingworth J, Bowden R. Action Recognition using Mined Hierarchical Compound Features. IEEE Trans Pattern Analysis and Machine Learning use 2D corners in both space and time. These are grouped spatially and temporally using a hierarchical process, with an increasing search area. At each stage of the hierarchy, the most distinctive and descriptive features are learned efficiently through data mining (Apriori rule).

Covert surveillance – Data mining and profiling

Data mining is the application of statistical techniques and programmatic algorithms to discover previously unnoticed relationships within the data

Covert surveillance – Data mining and profiling

Economic (such as credit card purchases) and social (such as telephone calls and emails) transactions in modern society create large amounts of stored data and records. In the past, this data was documented in paper records, leaving a paper trail, or was simply not documented at all. Correlation of paper-based records was a laborious process—it required human intelligence operators to manually dig through documents, which was time-consuming and incomplete, at best.

Covert surveillance – Data mining and profiling

But today many of these records are electronic, resulting in an electronic trail

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DATA CENTER

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DATA CENTER

Explicit Congestion Notification Data Center TCP

Data Center TCP (DCTCP) utilizes ECN to enhance the Transmission Control Protocol congestion control algorithm. It is used in data center networks. Whereas the standard TCP congestion control algorithm is only able to detect the presence of congestion, DCTCP, using ECN, is able to gauge the extent of congestion.

Converged infrastructure The evolution of data centers

Historically, to keep pace with the growth of business applications and the terabytes of data they generate, IT resources were deployed in a silo-like fashion. One set of resources has been devoted to one particular computing technology, business application or line of business. These resources support a single set of assumptions and cannot be optimized or reconfigured to support varying usage loads.

Converged infrastructure The evolution of data centers

The proliferation of IT sprawl in data centers has contributed to rising operations costs, reducing productivity, and stifling agility and flexibility

Converged infrastructure The evolution of data centers

A converged infrastructure addresses the problem of siloed architectures and IT sprawl by pooling and sharing IT resources. Rather than dedicating a set of resources to a particular computing technology, application or line of business, converged infrastructures creates a pool of virtualized server, storage and networking capacity that is shared by multiple applications and lines of business.

Data center History

Data centers have their roots in the huge computer rooms of the early ages of the computing industry

Data center History

The use of the term “data center,” as applied to specially designed computer rooms, started to gain popular recognition about this time.

Data center History

But nowadays, the division of these terms has almost disappeared and they are being integrated into a term “data center.”

Data center History

Data centers are typically very expensive to build and maintain.

Data center Power and cooling analysis

Among other things, a power and cooling analysis can help to identify hot spots, over-cooled areas that can handle greater power use density, the breakpoint of equipment loading, the effectiveness of a raised-floor strategy, and optimal equipment positioning (such as AC units) to balance temperatures across the data center

Data center

Large data centers are industrial scale operations using as much electricity as a small town and sometimes are a significant source of air pollution in the form of diesel exhaust.

Data center Requirements for modern data centers

A data center must therefore keep high standards for assuring the integrity and functionality of its hosted computer environment

Data center Requirements for modern data centers

The Telecommunications Industry Association’s TIA-942 Telecommunications Infrastructure Standard for Data Centers, specifies the minimum requirements for telecommunications infrastructure of data centers and computer rooms including single tenant enterprise data centers and multi-tenant Internet hosting data centers. The topology proposed in this document is intended to be applicable to any size data center.

Data center Requirements for modern data centers

Telcordia GR-3160, NEBS Requirements for Telecommunications Data Center Equipment and Spaces, provides guidelines for data center spaces within telecommunications networks, and environmental requirements for the equipment intended for installation in those spaces. These criteria were developed jointly by Telcordia and industry representatives. They may be applied to data center spaces housing data processing or Information Technology (IT) equipment. The equipment may be used to:

Data center Requirements for modern data centers

Operate and manage a carrier’s telecommunication network

Data center Requirements for modern data centers

Provide data center based applications directly to the carrier’s customers

Data center Requirements for modern data centers

Provide hosted applications for a third party to provide services to their customers

Data center Requirements for modern data centers

Effective data center operation requires a balanced investment in both the facility and the housed equipment. The first step is to establish a baseline facility environment suitable for equipment installation. Standardization and modularity can yield savings and efficiencies in the design and construction of telecommunications data centers.

Data center Requirements for modern data centers

For these reasons, telecommunications data centers should be planned in repetitive building blocks of equipment, and associated power and support (conditioning) equipment when practical

Data center Requirements for modern data centers

In addition to the energy savings, reduction in staffing costs and the ability to locate the site further from population centers, implementing a lights-out data center reduces the threat of malicious attacks upon the infrastructure.

Data center Requirements for modern data centers

There is a trend to modernize data centers in order to take advantage of the performance and energy efficiency increases of newer IT equipment and capabilities, such as cloud computing. This process is also known as data center transformation.

Data center Requirements for modern data centers

Organizations are experiencing rapid IT growth but their data centers are aging. Industry research company International Data Corporation (IDC) puts the average age of a data center at nine years old. Gartner, another research company says data centers older than seven years are obsolete.

Data center Requirements for modern data centers

In May 2011, data center research organization Uptime Institute, reported that 36 percent of the large companies it surveyed expect to exhaust IT capacity within the next 18 months.

Data center Requirements for modern data centers

Data center transformation takes a step-by-step approach through integrated projects carried out over time. This differs from a traditional method of data center upgrades that takes a serial and siloed approach. The typical projects within a data center transformation initiative include standardization/consolidation, virtualization, automation and security.

Data center Requirements for modern data centers

Standardization/consolidation: The purpose of this project is to reduce the number of data centers a large organization may have. This project also helps to reduce the number of hardware, software platforms, tools and processes within a data center. Organizations replace aging data center equipment with newer ones that provide increased capacity and performance. Computing, networking and management platforms are standardized so they are easier to manage.

Data center Requirements for modern data centers

Virtualize: There is a trend to use IT virtualization technologies to replace or consolidate multiple data center equipment, such as servers

Data center Requirements for modern data centers

Automating: Data center automation involves automating tasks such as provisioning, configuration, patching, release management and compliance. As enterprises suffer from few skilled IT workers, automating tasks make data centers run more efficiently.

Data center Requirements for modern data centers

Securing: In modern data centers, the security of data on virtual systems is integrated with existing security of physical infrastructures. The security of a modern data center must take into account physical security, network security, and data and user security.

Data center Carrier neutrality

Today many data centers are run by Internet service providers solely for the purpose of hosting their own and third party servers.

Data center Carrier neutrality

However traditionally data centers were either built for the sole use of one large company (i.e. Google, Amazon etc.) or as carrier hotels or Network-neutral data centers.

Data center Carrier neutrality

These facilities enable interconnection of carriers and act as regional fiber hubs serving local business in addition to hosting content servers.

Data center Data center tiers

Another consideration is the placement of the data center in a subterranean context, for data security as well as environmental considerations such as cooling requirements.

Data center Data center tiers

The German Datacenter star audit program uses an auditing process to certify 5 levels of “gratification” that affect Data Center criticality.

Data center Data center tiers

Independent from the ANSI/TIA-942 standard, the Uptime Institute, a think tank and professional-services organization based in Santa Fe, New Mexico, has defined its own four levels. The levels describe the availability of data from the hardware at a location. The higher the tier, the greater the availability. The levels are:

Data center Data center tiers

The difference between 99.671%, 99.741%, 99.982%, and 99.995%, while seemingly nominal, could be significant depending on the application.

Data center Data center tiers

Whilst no down-time is ideal, the tier system allows the below durations for services to be unavailable within one year (525,600 minutes):

Data center Data center tiers

Tier 1 (99.671%) status would allow 1729.224 minutes

Data center Data center tiers

Tier 2 (99.741%) status would allow 1361.304 minutes

Data center Data center tiers

Tier 3 (99.982%) status would allow 94.608 minutes

Data center Data center tiers

Tier 4 (99.995%) status would allow 26.28 minutes

Data center Design considerations

Very large data centers may use shipping containers packed with 1,000 or more servers each; when repairs or upgrades are needed, whole containers are replaced (rather than repairing individual servers).

Data center Design considerations

Local building codes may govern the minimum ceiling heights.

Data center Design programming

Other than the architecture of the building itself there are three elements to design programming for data centers: facility topology design (space planning), engineering infrastructure design (mechanical systems such as cooling and electrical systems including power) and technology infrastructure design (cable plant)

Data center Design programming

Various vendors who provide data center design services define the steps of data center design slightly differently, but all address the same basic aspects as given below.

Data center Modeling criteria

Modeling criteria are used to develop future-state scenarios for space, power, cooling, and costs. The aim is to create a master plan with parameters such as number, size, location, topology, IT floor system layouts, and power and cooling technology and configurations.

Data center Design recommendations

Design recommendations/plans generally follow the modelling criteria phase. The optimal technology infrastructure is identified and planning criteria is developed, such as critical power capacities, overall data center power requirements using an agreed upon PUE (power utilization efficiency), mechanical cooling capacities, kilowatts per cabinet, raised floor space, and the resiliency level for the facility.

Data center Conceptual design

Conceptual designs embody the design recommendations or plans and should take into account “what-if” scenarios to ensure all operational outcomes are met in order to future-proof the facility. Conceptual floor layouts should be driven by IT performance requirements as well as lifecycle costs associated with IT demand, energy efficiency, cost efficiency and availability. Future-proofing will also include expansion capabilities, often provided in modern data centers through modularity.

Data center Detail design

Detail design is undertaken once the appropriate conceptual design is determined, typically including a proof of concept. The detail design phase should include the development of facility schematics and construction documents as well as schematic of technology infrastructure, detailed IT infrastructure design and IT infrastructure documentation.

Data center Mechanical engineering infrastructure design

Mechanical engineering infrastructure design addresses mechanical systems involved in maintaining the interior environment of a data center, such as heating, ventilation and air conditioning (HVAC); humidification and dehumidification equipment; pressurization; and so on

Data center Electrical engineering infrastructure design

Electrical Engineering infrastructure design is focused on designing electrical configurations that accommodate various reliability requirements and data center sizes. Aspects may include utility service planning; distribution, switching and bypass from power sources; uninterruptable power source (UPS) systems; and more.

Data center Electrical engineering infrastructure design

These designs should dovetail to energy standards and best practices while also meeting business objectives. Electrical configurations should be optimized and operationally compatible with the data center user’s capabilities. Modern electrical design is modular and scalable, and is available for low and medium voltage requirements as well as DC (direct current).

Data center Technology infrastructure design

There are cabling systems for all data center environments, including horizontal cabling, voice, modem, and facsimile telecommunications services, premises switching equipment, computer and telecommunications management connections, keyboard/video/mouse connections and data communications

Data center Availability expectations

In other words, how can an appropriate level of availability best be met by design criteria to avoid financial and operational risks as a result of downtime? If the estimated cost of downtime within a specified time unit exceeds the amortized capital costs and operational expenses, a higher level of availability should be factored into the data center design

Data center Site selection

For example, the topology and the cost of managing a data center in a warm, humid climate will vary greatly from managing one in a cool, dry climate.

Data center Modularity and flexibility

Modularity and flexibility are key elements in allowing for a data center to grow and change over time. Data center modules are pre-engineered, standardized building blocks that can be easily configured and moved as needed.

Data center Modularity and flexibility

A modular data center may consist of data center equipment contained within shipping containers or similar portable containers. But it can also be described as a design style in which components of the data center are prefabricated and standardized so that they can be constructed, moved or added to quickly as needs change.

Data center Environmental control

Subterranean data centers may keep computer equipment cool while expending less energy than conventional designs.

Data center Environmental control

Modern data centers try to use economizer cooling, where they use outside air to keep the data center cool. At least one data center (located in Upstate New York) will cool servers using outside air during the winter. They do not use chillers/air conditioners, which creates potential energy savings in the millions.

Data center Environmental control

Telcordia GR-2930, NEBS: Raised Floor Generic Requirements for Network and Data Centers, presents generic engineering requirements for raised floors that fall within the strict NEBS guidelines.

Data center Environmental control

There are many types of commercially available floors that offer a wide range of structural strength and loading capabilities, depending on component construction and the materials used. The general types of raised floors include stringerless, stringered, and structural platforms, all of which are discussed in detail in GR-2930 and summarized below.

Data center Environmental control

Stringerless raised floors – One non-earthquake type of raised floor generally consists of an array of pedestals that provide the necessary height for routing cables and also serve to support each corner of the floor panels

Data center Environmental control

Stringered raised floors – This type of raised floor generally consists of a vertical array of steel pedestal assemblies (each assembly is made up of a steel base plate, tubular upright, and a head) uniformly spaced on two-foot centers and mechanically fastened to the concrete floor. The steel pedestal head has a stud that is inserted into the pedestal upright and the overall height is adjustable with a leveling nut on the welded stud of the pedestal head.

Data center Environmental control

Structural platforms – One type of structural platform consists of members constructed of steel angles or channels that are welded or bolted together to form an integrated platform for supporting equipment. This design permits equipment to be fastened directly to the platform without the need for toggle bars or supplemental bracing. Structural platforms may or may not contain panels or stringers.

Data center Metal whiskers

This phenomenon is not unique to data centers, and has also caused catastrophic failures of satellites and military hardware.

Data center Electrical power

Backup power consists of one or more uninterruptible power supplies, battery banks, and/or diesel / gas turbine generators.

Data center Electrical power

To prevent single points of failure, all elements of the electrical systems, including backup systems, are typically fully duplicated, and critical servers are connected to both the “A-side” and “B-side” power feeds. This arrangement is often made to achieve N+1 redundancy in the systems. Static switches are sometimes used to ensure instantaneous switchover from one supply to the other in the event of a power failure.

Data center Electrical power

Data centers typically have raised flooring made up of 60 cm (2 ft) removable square tiles. The trend is towards 80–100 cm (31–39 in) void to cater for better and uniform air distribution. These provide a plenum for air to circulate below the floor, as part of the air conditioning system, as well as providing space for power cabling.

Data center Low-voltage cable routing

Data cabling is typically routed through overhead cable trays in modern data centers. But some are still recommending under raised floor cabling for security reasons and to consider the addition of cooling systems above the racks in case this enhancement is necessary. Smaller/less expensive data centers without raised flooring may use anti-static tiles for a flooring surface. Computer cabinets are often organized into a hot aisle arrangement to maximize airflow efficiency.

Data center Fire protection

Passive fire protection elements include the installation of fire walls around the data center, so a fire can be restricted to a portion of the facility for a limited time in the event of the failure of the active fire protection systems

Data center Security

Physical security also plays a large role with data centers. Physical access to the site is usually restricted to selected personnel, with controls including bollards and mantraps. Video camera surveillance and permanent security guards are almost always present if the data center is large or contains sensitive information on any of the systems within. The use of finger print recognition man traps is starting to be commonplace.

Data center Energy use

By 2012 the cost of power for the data center is expected to exceed the cost of the original capital investment.

Data center Greenhouse gas emissions

Given a business as usual scenario greenhouse gas emissions from data centers is projected to more than double from 2007 levels by 2020.

Data center Greenhouse gas emissions

Siting is one of the factors that affect the energy consumption and environmental effects of a datacenter. In areas where climate favors cooling and lots of renewable electricity is available the environmental effects will be more moderate. Thus countries with favorable conditions, such as: Canada, Finland, Sweden and Switzerland, are trying to attract cloud computing data centers.

Data center Greenhouse gas emissions

In an 18-month investigation by scholars at Rice University’s Baker Institute for Public Policy in Houston and the Institute for Sustainable and Applied Infodynamics in Singapore, data center-related emissions will more than triple by 2020.

Data center Energy efficiency

The most commonly used metric to determine the energy efficiency of a data center is power usage effectiveness, or PUE. This simple ratio is the total power entering the data center divided by the power used by the IT equipment.

Data center Energy efficiency

Some large data center operators like Microsoft and Yahoo! have published projections of PUE for facilities in development; Google publishes quarterly actual efficiency performance from data centers in operation.

Data center Energy efficiency

The U.S. Environmental Protection Agency has an Energy Star rating for standalone or large data centers. To qualify for the ecolabel, a data center must be within the top quartile of energy efficiency of all reported facilities.

Data center Energy efficiency

European Union also has a similar initiative: EU Code of Conduct for Data Centres

Data center Energy use analysis

Often, the first step toward curbing energy use in a data center is to understand how energy is being used in the data center. Multiple types of analysis exist to measure data center energy use. Aspects measured include not just energy used by IT equipment itself, but also by the data center facility equipment, such as chillers and fans.

Data center Energy efficiency analysis

An energy efficiency analysis measures the energy use of data center IT and facilities equipment. A typical energy efficiency analysis measures factors such as a data center’s power use effectiveness (PUE) against industry standards, identifies mechanical and electrical sources of inefficiency, and identifies air-management metrics.

Data center Computational fluid dynamics (CFD) analysis

By predicting the effects of these environmental conditions, CFD analysis in the data center can be used to predict the impact of high-density racks mixed with low-density racks and the onward impact on cooling resources, poor infrastructure management practices and AC failure of AC shutdown for scheduled maintenance.

Data center Thermal zone mapping

Thermal zone mapping uses sensors and computer modeling to create a three-dimensional image of the hot and cool zones in a data center.

Data center Thermal zone mapping

This information can help to identify optimal positioning of data center equipment. For example, critical servers might be placed in a cool zone that is serviced by redundant AC units.

Data center Green datacenters

Datacenters use a lot of power, consumed by two main usages: the power required to run the actual equipment and then the power required to cool the equipment

Data center Network infrastructure

Communications in data centers today are most often based on networks running the IP protocol suite. Data centers contain a set of routers and switches that transport traffic between the servers and to the outside world. Redundancy of the Internet connection is often provided by using two or more upstream service providers (see Multihoming).

Data center Network infrastructure

Some of the servers at the data center are used for running the basic Internet and intranet services needed by internal users in the organization, e.g., e-mail servers, proxy servers, and DNS servers.

Data center Network infrastructure

Network security elements are also usually deployed: firewalls, VPN gateways, intrusion detection systems, etc. Also common are monitoring systems for the network and some of the applications. Additional off site monitoring systems are also typical, in case of a failure of communications inside the data center.

Data center Data center infrastructure management

Data center infrastructure management (DCIM) is the integration of information technology (IT) and facility management disciplines to centralize monitoring, management and intelligent capacity planning of a data center’s critical systems. Achieved through the implementation of specialized software, hardware and sensors, DCIM enables common, real-time monitoring and management platform for all interdependent systems across IT and facility infrastructures.

Data center Data center infrastructure management

Depending on the type of implementation, DCIM products can help data center managers identify and eliminate sources of risk to increase availability of critical IT systems. DCIM products also can be used to identify interdependencies between facility and IT infrastructures to alert the facility manager to gaps in system redundancy, and provide dynamic, holistic benchmarks on power consumption and efficiency to measure the effectiveness of “green IT” initiatives.

Data center Data center infrastructure management

Server, storage, and staff utilization metrics can contribute to a more complete view of an enterprise data center

Data center Applications

The main purpose of a data center is running the applications that handle the core business and operational data of the organization. Such systems may be proprietary and developed internally by the organization, or bought from enterprise software vendors. Such common applications are ERP and CRM systems.

Data center Applications

A data center may be concerned with just operations architecture or it may provide other services as well.

Data center Applications

Often these applications will be composed of multiple hosts, each running a single component. Common components of such applications are databases, file servers, application servers, middleware, and various others.

Data center Applications

Encrypted backups can be sent over the Internet to another data center where they can be stored securely.

Data center Applications

For quick deployment or disaster recovery, several large hardware vendors have developed mobile solutions that can be installed and made operational in very short time. Companies such as Cisco Systems, Sun Microsystems (Sun Modular Datacenter), Bull, IBM (Portable Modular Data Center), HP (Performance Optimized Datacenter), and Google (Google Modular Data Center) have developed systems that could be used for this purpose.

Data center services

Data center services encompass all of the technology and facility-related components or activities that support the projects and operation of a data center, which is an environment that provides processing, storage, networking, management and the distribution of data within an enterprise.

Data center services

Generally, data center services fall into two categories: services provided to a data center or services provided from a data center. Services to a data center can include any services that help to plan, design, manage, support, update or modernize data center equipment, software or facilities. Services from a data center can encompass any compute service that data centers deliver, such as data backup and archiving, managed e-mail or cloud computing.

Data center services – Support services

Support services for the data center can be generally defined as technical support, which provides assistance to help solve problems related to technology products. Technical support services for data centers help to address challenges with the servers, storage, software and networking equipment that comprise a data center or the related processes involved in managing data center equipment. Data center support services can also include installing and configuring technical equipment.

Data center services – Technical consulting services

Examples of technical consulting services specific to data center services might include selecting a new data center location, consolidation, virtualization, automation, redesigning data centers for cloud computing, implementing storage arrays, or incorporating offsite storage services into an existing network.

Data center services – Outsourcing services

IT outsourcing occurs when one company (the outsourcing customer) contracts with an outsourcing vendor to provide IT services that the customer would otherwise deliver in-house. Such IT services could be disaster recovery, data storage or other IT functions. Outsourcing services for the data center can range from hosting, managing and maintaining an entire data center to more discrete data center tasks such as upgrading servers or backing up data.

Data center services – Application services

The definition of application services varies depending on the type of company offering the services

Data center services – Technical training services

Within the umbrella of data center services, technical training services can provide skills relevant to any of the hardware, software or processes related to managing a data center, or fixing, updating, integrating or managing any of the equipment within a data center.

Data center services – Financing and leasing services

Financing and leasing services within the context of data center services might include leasing a data center facility; leasing data center equipment, such as servers; or financing a data center project, such as building or upgrading a data center facility.

Green computing – Data center design

Data center facilities are heavy consumers of energy, accounting for between 1.1% and 1.5% of the world’s total energy use in 2010 . The U.S. Department of Energy estimates that data center facilities consume up to 100 to 200 times more energy than standard office buildings.

Green computing – Data center design

Energy efficient data center design should address all of the energy use aspects included in a data center: from the IT equipment to the HVAC equipment to the actual location, configuration and construction of the building.

Green computing – Data center design

The U.S. Department of Energy specifies five primary areas on which to focus energy efficient data center design best practices:

Green computing – Data center design

Additional energy efficient design opportunities specified by the U.S. Department of Energy include on-site electrical generation and recycling of waste heat.

Green computing – Data center design

Energy efficient data center design should help to better utilize a data center’s space, and increase performance and efficiency.

Green computing – Data center power

federal government has set a minimum 10% reduction target for data center energy usage by 2011

Cisco Career Certifications – Data Center

CCNP Data Center validates knowledge of data center design, equipment installation, and maintenance.

Cisco Career Certifications – Data Center

Four exams are required:

Cisco Career Certifications – Data Center

Implementing Cisco Data Center Unified Computing (DCUCI) v5.0

Cisco Career Certifications – Data Center

and choose either designing or troubleshooting:

Cisco Career Certifications – Data Center

Designing Cisco Data Center Unified Computing (DCUCD) v5.0

Cisco Career Certifications – Data Center

Troubleshooting Cisco Data Center Unified Computing (DCUCT) v5.0

Cisco Career Certifications – Data Center

Cisco has announced the September availability of a CCIE Data Center certification, which certifies the expert-level skills required to plan, prepare, operate, monitor, and troubleshoot complex data center networks. The CCIE Data Center written exam will be available Sept. 3, 2012; the lab exam is expected to be available in October.

CCIE – Data Center

The CCIE Data Center certifies the expert-level skills required to plan, prepare, operate, monitor, and troubleshoot complex data center networks. Professionals who achieve CCIE Data Center certification have demonstrated their technical skills at the highest level.

CCNA Security – Data Center

* Implementing Cisco Data Center Unified Computing (DCUCI) v5.0

CCNA Security – Data Center

* Designing Cisco Data Center Unified Computing (DCUCD) v5.0

CCNA Security – Data Center

* Troubleshooting Cisco Data Center Unified Computing (DCUCT) v5.0

Google platform – Modular container data centers

Since 2005, http://www.theregister.co.uk/2009/04/10/google_data_center_video Google has been moving to a containerized Google Modular Data Center|modular data center. Google filed a patent application for this technology in 2003.

Google Modular Data Center

http://www.youtube.com/watch?v=bs3Et540-_s The data centers are rumored to cost $600 million USD each, and use from 50 to 103 megawatts of electricity

Google Modular Data Center – History

Google was reported in November 2005 to be working on their own shipping container datacenter. Although in January 2007 it was reported that the project had been discontinued, Google’s patent on the concept was still pushed through the patent system and was successfully issued in October 2007. In 2009 Google announced that their first container based data center has been in production since 2005.

Google Modular Data Center – Locations

The locations of Google’s various data centers are as follows:[ http://www.google.com/about/datacenters/locations/index.html Locations · Google Data Centers]

Google Modular Data Center – Locations

* Douglas County, Georgia

Google Modular Data Center – Locations

* Pryor Creek, Oklahoma at MidAmerica Industrial Park.

Google Modular Data Center – Locations

* Berkeley County, South Carolina

Google Modular Data Center – Locations

‘South America:’

Google Modular Data Center – Locations

* Jurong West, Singapore

Equinix – Europe data centers

*Germany – (10 locations) 5 in Frankfurt (City, North, Morfelden), 3 in Munich, 2 in Düsseldorf

Equinix – Europe data centers

*Switzerland – (5 locations) 3 in Zurich and 2 in Geneva

Equinix – Europe data centers

*UK – (5 locations) 3 in London (City, West, Park Royal), 2 in Slough

Equinix – U.S. and Canada data centers

*Ashburn, Virginia (Washington, D.C.|Washington, D.C. Metro Area) (3 locations)

Equinix – U.S. and Canada data centers

*Silicon Valley, California (3 locations: Palo Alto, California, San Jose, California, Sunnyvale, California)

Equinix – Asia-Pacific data centers

*Sydney, Australia (3 locations)

Equinix – South America data centers

*Brazil – (3 locations) 2 in São Paulo and 1 in Rio de Janeiro

Datacenter – Requirements for modern data centers

A data center must therefore keep high standards for assuring the integrity and functionality of its hosted computer environment

Datacenter – Requirements for modern data centers

The topology proposed in this document is intended to be applicable to any size data center

Datacenter – Requirements for modern data centers

They may be applied to data center spaces housing data processing or Information Technology (IT) equipment

Datacenter – Requirements for modern data centers

*Operate and manage a carrier’s telecommunication network

Datacenter – Requirements for modern data centers

*Provide data center based applications directly to the carrier’s customers

Datacenter – Requirements for modern data centers

*Provide hosted applications for a third party to provide services to their customers

Datacenter – Requirements for modern data centers

In addition to the energy savings, reduction in staffing costs and the ability to locate the site further from population centers, implementing a lights-out data center reduces the threat of malicious attacks upon the infrastructure.

Datacenter – Requirements for modern data centers

In May 2011, data center research organization Uptime Institute, reported that 36 percent of the large companies it surveyed expect to exhaust IT capacity within the next 18 months.Niccolai, James. Data Centers Turn to Outsourcing to Meet Capacity Needs, CIO.com, May 10, 2011 [ http://www.cio.com/article/681897/Data_Centers_Turn_to_Outsourcing_to_Meet_Capacity_Needs]

Datacenter – Requirements for modern data centers

Three Signs it’s time to transform your data center, August 3, 2010, Data Center Knowledge [ http://www.datacenterknowledge.com/archives/2010/08/03/three-signs-it%E2%80%99s-time-to-transform-your-data-center/] The typical projects within a data center transformation initiative include standardization/consolidation, virtualization, automation and security.

Datacenter – Requirements for modern data centers

Complexity: Growing Data Center Challenge, Data Center Knowledge, May 16, 2007

Datacenter – Requirements for modern data centers

*Virtualize: There is a trend to use IT virtualization technologies to replace or consolidate multiple data center equipment, such as servers. Virtualization helps to lower capital and operational expenses,Sims, David. Carousel’s Expert Walks Through Major Benefits of Virtualization, TMC Net, July 6, 2010

Datacenter – Requirements for modern data centers

Gartner: Virtualization Disrupts Server Vendors, Data Center Knowledge, December 2, 2008 [ http://www.datacenterknowledge.com/archives/2008/12/02/gartner-virtualization-disrupts-server-vendors/]

Datacenter – Requirements for modern data centers

*Automating: Data center automation involves automating tasks such as provisioning, configuration, Patch (computing)|patching, release management and compliance. As enterprises suffer from few skilled IT workers, automating tasks make data centers run more efficiently.

Datacenter – Requirements for modern data centers

*Securing: In modern data centers, the security of data on virtual systems is integrated with existing security of physical infrastructures.Ritter, Ted. Nemertes Research, Securing the Data-Center Transformation Aligning Security and Data-Center Dynamics, [ http://lippisreport.com/2011/05/securing-the-data-center-transformation-aligning-security-and-data-center-dynamics/] The security of a modern data center must take into account physical security, network security, and data and user security.

Datacenter – Data center tiers

Another consideration is the placement of the data center in a subterranean context, for data security as well as environmental considerations such as cooling requirements.A ConnectKentucky article mentioning Stone Mountain Data Center Complex

Datacenter – Data center tiers

Independent from the ANSI/TIA-942 standard, the Uptime Institute, a think tank and professional-services organization based in Santa Fe, New Mexico|Santa Fe, New Mexico, has defined its own four levels. The levels describe the availability of data from the hardware at a location. The higher the tier, the greater the availability. The levels are:

Datacenter – Data center tiers

A document from the Uptime Institute describing the different tiers (click through the download page)

Datacenter – Data center tiers

The rating guidelines from the Uptime Institute

Datacenter – Data center tiers

* Tier 1 (99.671%) status would allow 1729.224 minutes

Datacenter – Data center tiers

* Tier 2 (99.741%) status would allow 1361.304 minutes

Datacenter – Data center tiers

* Tier 3 (99.982%) status would allow 94.608 minutes

Datacenter – Data center tiers

* Tier 4 (99.995%) status would allow 26.28 minutes

War of Currents – DC in data centers

A computer data center may have hundreds or thousands of processors in operation.

War of Currents – DC in data centers

Since such centers typically are vital to the operation of a business or institution, they require highly reliable power distribution. The energy consumed by a data center is a significant part of its operation cost, and heat dissipated by power supplies must be removed by air conditioning equipment, resulting in additional energy consumption.

War of Currents – DC in data centers

Studies by the Electric Power Research Instititute (EPRI) have suggested that the multiple levels of power distribution in a data center can be replaced by a 380 volt DC distribution system.

War of Currents – DC in data centers

Multiple levels of AC transformation and uninterruptible power supply (UPS) can instead be replaced by a building-wide 380 volt DC system connected directly to the processor power supplies.

War of Currents – DC in data centers

A minimum of 7 or 8% annual energy consumption can be saved by eliminating the multiple stages of conventional AC power distribution.

War of Currents – DC in data centers

A great increase in reliability can also result, since the inverter output stages of UPS are the source of many data center failures.

War of Currents – DC in data centers

The 380 V building DC network could be directly connected to batteries to provide uninterruptible power.

War of Currents – DC in data centers

A local DC microgrid could also offset utility energy purchase with local generation from solar panels, wind turbines, or other distributed generation sources.

War of Currents – DC in data centers

The 380 volt level was selected because it greatly reduces the size of conductors compared with a 48 volt distribution system (the standard in the telecommunications industry.) The system would be operated with a split supply, at +190 V and -190V with respect to ground, to minimize the shock hazard to people.

War of Currents – DC in data centers

The 380 volt level is compatible with the typical ratings of components now used in computer power supplies.IEEE Power and Energy Magazine November/December 2012

Data centers

Large data centers are industrial scale operations using as much electricity as a small town and sometimes are a significant source of air pollution in the form of diesel exhaust.

Data centers – Design considerations

Very large data centers may use intermodal container|shipping containers packed with 1,000 or more servers each; when repairs or upgrades are needed, whole containers are replaced (rather than repairing individual servers).

Data centers – Design programming

2, 2009 Other than the architecture of the building itself there are three elements to design programming for data centers: facility topology design (space planning), engineering infrastructure design (mechanical systems such as cooling and electrical systems including power) and technology infrastructure design (cable plant)

Data centers – Modularity and flexibility

“HP says prefab data center cuts costs in half,” InfoWorld, July 27, 2010

Data centers – Environmental control

Subterranean data centers may keep computer equipment cool while expending less energy than conventional designs.

Data centers – Environmental control

Telcordia [ http://telecom-info.telcordia.com/site-cgi/ido/docs.cgi?ID=SEARCHDOCUMENT=GR-2930 GR-2930, NEBS: Raised Floor Generic Requirements for Network and Data Centers], presents generic engineering requirements for raised floors that fall within the strict NEBS guidelines.

Data centers – Environmental control

*’Stringerless raised floors’ – One non-earthquake type of raised floor generally consists of an array of pedestals that provide the necessary height for routing cables and also serve to support each corner of the floor panels

Data centers – Environmental control

*’Stringered raised floors’ – This type of raised floor generally consists of a vertical array of steel pedestal assemblies (each assembly is made up of a steel base plate, tubular upright, and a head) uniformly spaced on two-foot centers and mechanically fastened to the concrete floor. The steel pedestal head has a stud that is inserted into the pedestal upright and the overall height is adjustable with a leveling nut on the welded stud of the pedestal head.

Data centers – Environmental control

*’Structural platforms’ – One type of structural platform consists of members constructed of steel angles or channels that are welded or bolted together to form an integrated platform for supporting equipment. This design permits equipment to be fastened directly to the platform without the need for toggle bars or supplemental bracing. Structural platforms may or may not contain panels or stringers.

Data centers – Environmental control

Data centers typically have raised flooring made up of removable square tiles. The trend is towards void to cater for better and uniform air distribution. These provide a plenum space|plenum for air to circulate below the floor, as part of the air conditioning system, as well as providing space for power cabling.

IT energy management – Server and data center power management

Servers and data centers account for 23% of IT energy demand. As hardware becomes smaller and less expensive, energy costs constitute a larger portion of server or data center costs.

IT energy management – Server and data center power management

Server and data center systems tend to be designed with significant computational redundancy. Typically, an individual server will only operate at around 18% of its capacity. The reasons for this are largely historical, and with current technology, this level of redundancy is not required.

IT energy management – Server and data center power management

This is typically done by doing diagnostic tests on individual servers and developing a model for a data center’s energy demand using these measurements

IT energy management – Server and data center power management

Energy efficiency benchmarks, such as SPECpower, or specifications, like Average CPU power, can be used to comparing server efficiency and performance per watt.

Data center infrastructure efficiency

DCIE is the percentage value derived, by dividing information technology equipment power by total facility power.SearchdataCenter.com – [ http://searchdatacenter.techtarget.com/sDefinition/0,,sid80_gci1307931,00.html data center infrastructure efficiency (DCIE)][ http://www.thegreengrid.org/sitecore/content/Global/Content/white-papers/The-Green-Grid-Data-Center-Power-Efficiency-Metrics-PUE-and-DCiE.aspx The Green Grid – The Green Grid Data Center Power Efficiency Metrics: PUE and DCiE]DatacenterDynamics FOCUS – [ http://www.datacenterdynamics.com/ME2/Audiences/dirmod.asp?sid=nm=type=Publishingmod=Publications%3A%3AArticlemid=8F3A7027421841978F18BE895F87F791tier=4id=AA31C266434D4801B751E418AAE26DFAAudID=E5BD2FF22AF74DF3A0D5F4E519A61511 Data Center Efficiency: If you can’t measure it, you can’t improve it]

Utah Data Center

The ‘Utah Data Center’, also known as the ‘Intelligence Community Comprehensive National Cybersecurity Initiative Data Center’, is a Computer data storage|data storage facility for the United States Intelligence Community that is designed to store extremely large amounts of data, estimated to be on the order of exabytes or higher

Utah Data Center

The megaproject was completed in late-2013 at a cost of US$1.5 billion despite ongoing controversy over the NSA’s involvement in the practice of mass surveillance in the United States. Prompted by the 2013 mass surveillance disclosures by ex-NSA contractor Edward Snowden, the Utah Data Center was hailed by The Wall Street Journal as a symbol of the spy agency’s surveillance prowess.

Utah Data Center – Purpose

In response to claims that the data center would be used to illegally monitor emails of US citizens, in April 2013 an NSA spokesperson said, Many unfounded allegations have been made about the planned activities of the Utah Data Center, ..

Utah Data Center – Purpose

In April 2009, officials at the United States Department of Justice acknowledged that the NSA had engaged in large-scale overcollection of domestic communications in excess of the FISA court|federal intelligence court’s authority, but claimed that the acts were unintentional and had since been rectified.

Utah Data Center – Purpose

In August 2012, The New York Times published short documentaries by independent filmmakers entitled The Program,Poitras, Laura, [ http://www.nytimes.com/2012/08/23/opinion/the-national-security-agencys-domestic-spying-program.html The Program], New York Times Op-Docs, August 22, 2012 based on interviews with a whistleblower named William Binney (U.S

Utah Data Center – Purpose

Reports linked the data center to the NSA’s controversial expansion of activities, which store extremely large amounts of data

Utah Data Center – Purpose

The UDC is expected to store internet data as well as phone records from the controversial NSA call database when it opens in 2013.

Utah Data Center – Structure

The planned structure is 1 million or 1.5 million square feet, 100,000 square feet of data center space and greater than 900,000 square feet of technical support and administrative space, and it is projected to cost from $1.5 billion to $2 billion when finished in September 2013

Data center bridging

The higher level goal is to use a single set of Ethernet physical devices or adapters for computers to talk to a Storage Area Network, Local Area network and InfiniBand fabric.Silvano Gai, Data Center Networks and Fibre Channel over Ethernet (FCoE) (Nuova Systems, 2008)

Data center bridging

Ethernet is the primary network protocol in data centers for computer-to-computer communications. However, Ethernet is designed to be a best-effort network that may experience packet loss when the network or devices are busy. In Internet Protocol networks, transport reliability under the end-to-end principle is the responsibility of the transport protocols, such as the Transmission Control Protocol (TCP).

Data center bridging

One area of evolution for Ethernet is to add extensions to the existing protocol suite to provide reliability without requiring the complexity of TCP. With the move to 10 Gbit/s and faster transmission rates, there is also a desire for finer granularity in control of bandwidth allocation and to ensure it is used more effectively. Beyond the benefits to traditional application traffic, these enhancements make Ethernet a more viable transport for storage and server cluster traffic.

Data center bridging

To meet these goals new standards are being (or have been) developed that either extend the existing set of Ethernet protocols or emulate the connectivity offered by Ethernet protocols. They are being (or have been) developed respectively by two separate standards bodies, the Institute of Electrical and Electronics Engineers (IEEE) Data Center Bridging Task Group of the IEEE 802.1 Working Group and the Internet Engineering Task Force (IETF).

Data center bridging – Terminology

Different terms have been used to market products based on data center bridging standards:

Data center bridging – Terminology

* ‘Data Center Ethernet’ (DCE) was a term trademarked by Brocade Communications Systems in 2007 but abandoned by request in 2008. DCE referred to Ethernet enhancements for the Data Center Bridging standards, and also including a Layer 2 Multipathing implementation based on the IETF’s TRILL (computing)|Transparent Interconnection of Lots of Links (TRILL) standard.

Data center bridging – Terminology

* ‘Convergence Enhanced Ethernet’ or ‘Converged Enhanced Ethernet’ (CEE) was defined from 2008 through January 2009 by group of including Broadcom, Brocade, Cisco Systems, Emulex, HP, IBM, Juniper Networks, QLogic. The ad-hoc group formed to create proposals for enhancements that enable networking protocol convergence over Ethernet, specially Fibre Channel. Proposed specifications to IEEE 802.1 working groups initially included:

Data center bridging – Terminology

** The Priority-based Flow Control (PFC) [ http://www.ieee802.org/1/files/public/docs2008/bb-pelissier-pfc-proposal-0508.pdf Version 0 Specification] was submitted for use in the [ http://www.ieee802.org/1/pages/802.1bb.html IEEE 802.1Qbb] project, under the DCB task group of the IEEE 802.1 working group.

Data center bridging – Terminology

** The [ http://www.ieee802.org/1/files/public/docs2008/az-wadekar-ets-proposal-0608-v1.01.pdf Enhanced Transmission Selection (ETS) Version 0 Specification] was submitted for use in the [ http://www.ieee802.org/1/pages/802.1az.html IEEE 802.1Qaz] project, under the DCB task group of the IEEE 802.1 working group.

Data center bridging – Terminology

** The [ http://www.ieee802.org/1/files/public/docs2008/az-wadekar-dcbx-capability-exchange-discovery-protocol-1108-v1.01.pdf Data Center Bridging eXchange (DCBX) Version 0 Specification] was also submitted for use in the [ http://www.ieee802.org/1/pages/802.1az.html IEEE 802.1Qaz] project.

Data center bridging – IEEE Task Group

* Priority-based Flow Control (PFC): [ http://www.ieee802.org/1/pages/802.1bb.html IEEE 802.1Qbb] provides a link level flow control mechanism that can be controlled independently for each frame priority. The goal of this mechanism is to ensure zero loss under congestion in DCB networks.

Data center bridging – IEEE Task Group

* Congestion Notification: [ http://www.ieee802.org/1/pages/802.1au.html IEEE 802.1Qau] provides end to end congestion management for protocols that are capable of transmission rate limiting to avoid frame loss. It is expected to benefit protocols such as TCP that do have native congestion management as it reacts to congestion in a more timely manner.

Data center bridging – IEEE Task Group

* Data Center Bridging Capabilities Exchange Protocol (DCBX): a discovery and capability exchange protocol that is used for conveying capabilities and configuration of the above features between neighbors to ensure consistent configuration across the network. This protocol leverages functionality provided by [ http://www.ieee802.org/1/pages/802.1ab.html IEEE 802.1AB] (LLDP). It is actually included in the 802.1az standard.

Data center bridging – Other groups

* The IETF TRILL (computing)|TRILL (Transparent Interconnection of Lots of Links) standard provides least cost pair-wise data forwarding without configuration in multi-hop networks with arbitrary topology, safe forwarding even during periods of temporary loops, and support for multipathing of both unicast and multicast traffic

Data center bridging – Other groups

* IEEE 802.1aq Shortest Path Bridging ([ http://www.ieee802.org/1/pages/802.1aq.html IEEE 802.1aq]) 802.1aq specifies shortest path bridging of unicast and multicast Ethernet frames, to calculate multiple active topologies (virtual LANs) that can share learnt station location information

Data center bridging – Other groups

* Fibre Channel over Ethernet: [ http://www.t11.org/fcoe T11 FCoE] This project utilizes existing Fibre Channel protocols to run on Ethernet to enable servers to have access to Fibre Channel storage via Ethernet

Data center bridging – Other groups

* IEEE 802.3bd provided a mechanism for link-level per priority pause flow control.

Data center bridging – Other groups

These new protocols required new hardware and software in both the network and the network interface controller. Products were being developed by companies such as Avaya, Brocade, Cisco, Dell, EMC Corporation|EMC, Emulex, HP, Huawei, IBM, and Qlogic.

Bolt (web browser) – Data centers for servers

The proxy servers that BOLT cloud-based architecture uses are located in the United States.

Opera Mini – Data centers

* June 30, 2009 – TeliaSonera International Carrier will provide Opera with co-location for establishing a new data center in Poland

National Oceanographic Data Center

The ‘National Oceanographic Data Center’ (NODC) is one of the national environmental data centers operated by the National Oceanic and Atmospheric Administration (NOAA) of the United States Department of Commerce|U.S

National Oceanographic Data Center

Also, the [ http://www-nsidc.colorado.edu/ National Snow and Ice Data Center]* (NSIDC) in Boulder, Colorado is operated for NGDC by the University of Colorado through the [ http://cires.colorado.edu/ Cooperative Institute for Research in Environmental Sciences] (CIRES*).

National Oceanographic Data Center

These discipline-oriented centers serve as national repositories and dissemination facilities for global environmental data. The data archives amassed by the NODC and the other centers provide a record of Earth’s changing environment, and support numerous research and operational applications. Working cooperatively, the centers provide data products and services to scientists, engineers, resource managers, policy makers, and other users in the United States and around the world.

National Oceanographic Data Center – History

Established in 1961, the NODC was originally an interagency facility administered by the United States Navy|U.S

National Oceanographic Data Center – Core Description

The Data center|Data Center represents [ http://www.nesdis.noaa.gov/ NESDIS] and [ http://www.noaa.gov/ NOAA] to the general public, government agencies, private institutions, foreign governments, and the private sector on matters involving oceanographic data.

National Oceanographic Data Center – NODC Data Holdings

The NODC manages the world’s largest collection of publicly available oceanographic data

National Oceanographic Data Center – NODC Data Holdings

Through NODC archive and access services these ocean data are being reused to answer questions about [ http://www.nodc.noaa.gov/General/Oceanthemes/climate.html climate change], ocean phenomena, and management of [ http://www.nodc.noaa.gov/General/Oceanthemes/conserve.html coastal and marine resources], [ http://www.nodc.noaa.gov/General/Oceanthemes/maroperations.html marine transportation], [ http://www.nodc.noaa.gov/General/Oceanthemes/health.html recreation], [ http://www.nodc.noaa.gov/General/Oceanthemes/natsecurity.html national security], and [ http://www.nodc.noaa.gov/General/Oceanthemes/hazards.html natural disasters]

National Oceanographic Data Center – International Cooperation and Data Exchange

A significant percentage of the oceanographic data held by NODC is foreign. NODC acquires foreign data through direct bilateral exchanges with other countries, and through the facilities of the [ http://www.nodc.noaa.gov/General/NODC-dataexch/NODC-wdca.html World Data Center for Oceanography], Silver Spring, which is collocated with and operated by NODC.

National Oceanographic Data Center – International Cooperation and Data Exchange

World Data Center, Silver Spring, Maryland, United States,

National Oceanographic Data Center – International Cooperation and Data Exchange

World Data Center, Moscow|Moscow, Russia, and

National Oceanographic Data Center – International Cooperation and Data Exchange

World Data Center, Tianjin, People’s Republic of China.

National Oceanographic Data Center – International Cooperation and Data Exchange

They are part of the [ http://www.ngdc.noaa.gov/wdc/wdcmain.html World Data Center System] initiated in 1957 to provide a mechanism for data exchange, and they operate under guidelines issued by the International Council for Science|International Council of Scientific Unions (ICSU).

National Oceanographic Data Center – International Cooperation and Data Exchange

Under NODC leadership, the [ http://www.nodc.noaa.gov/General/NODC-dataexch/NODC-godar.html Global Data Archeology and Rescue (GODAR)] project has grown into a major international program sponsored by the Intergovernmentalism|Inter-governmental Oceanographic Commission

National Oceanographic Data Center – Data Management for Global Change Studies

The NODC provides data management support for major ocean science projects and promotes improved working relations with the academic ocean research community.

National Oceanographic Data Center – NOAA Library and Information Network

The NODC also manages the [ http://www.lib.noaa.gov/ NOAA Library] and Information Network, which includes the NOAA Central Library in Silver Spring, MD; regional libraries in Miami, FL and Seattle, WA; and field libraries or information centers at about 30 NOAA sites throughout the United States. The combined libraries contain millions of volumes including books, journals, CD-ROMs, DVDs, audio, and Videocassette recorder|video tapes.

National Oceanographic Data Center – User Services

Each year the NODC responds to thousands of requests for oceanographic data and information. Copies of specified data sets or data selected from the NODC’s archive databases can be provided to users on various media types, or online. NODC data products are provided at prices that cover the cost of data selection and retrieval. However, data provided on the NODC public website is free of charge.

Google Inc. – Google data centers

Google said they will be operational within two years.[ http://www.datacenterknowledge.com/archives/2011/09/28/google-to-build-three-data-centers-in-asia/ Google to Build Three Data Centers in Asia]

Google Inc. – Google data centers

In October 2013, The Washington Post reported that the U.S. National Security Agency intercepted communications between Google’s data centers, as part of a program named Muscular (surveillance program)|MUSCULAR. This wiretapping was made possible because Google did not encrypt data passed inside its own network. Google began encrypting data sent between data centers in 2013.

National Climatic Data Center

The United States ‘National Climatic Data Center’ (‘NCDC’), previously known as the National Weather Records Center (NWRC), in Asheville, North Carolina is the world’s largest active archive of weather data. Starting as a tabulation unit in New Orleans, Louisiana in 1934, the climate records were transferred to Asheville in 1951, becoming named the National Weather Records Center (NWRC). It was later renamed the National Climatic Data Center, with relocation occurring in 1993.

National Climatic Data Center – History

In 1934, a tabulation unit was established in New Orleans, Louisiana to process past weather records

National Climatic Data Center – Archived data

The Center has more than 150 years of data on hand with 224 gigabytes of new information added each day. NCDC archives 99 percent of all NOAA data, including over 320 million paper records; 2.5 million microfiche records; over 1.2 petabytes of Digital data|digital data residing in a mass storage System platform|environment. NCDC has satellite weather images back to 1960.

National Climatic Data Center – Sources

Data are received from a wide variety of sources, including weather satellites, radar, automated airport weather stations, National Weather Service|NWS cooperative observers, aircraft, ships, radiosondes, wind profilers, rocketsondes, solar radiation networks, and NWS Forecast/Warnings/Analyses Products.

National Climatic Data Center – Climate focus

The Center provides historical perspectives on climate which are vital to studies on global climate change, the greenhouse effect, and other environmental issues. The Center stores information essential to industry, agriculture, science, hydrology, transportation, recreation, and engineering.

National Climatic Data Center – Climate focus

Working with international institutions such as the International Council of Scientific Unions, the World Data Centers, and the World Meteorological Organization, NCDC develops standards by which data can be exchanged and made accessible.

National Climatic Data Center – Climate focus

NCDC provides the historical perspective on climate. Through the use of over a hundred years of weather observations, reference data bases are generated. From this knowledge the clientele of NCDC can learn from the past to prepare for a better tomorrow. Wise use of our most valuable natural resource, climate, is the goal of climate researchers, state and regional climate centers, business, and commerce.[ http://www.ncdc.noaa.gov/oa/about/whatisncdc.html NCDC: What Is NCDC?]

National Climatic Data Center – Associated entities

NCDC also maintains World Data Center for Meteorology, Asheville. The four World Centers (U.S., Russia, Japan and China) have created a free and open situation in which data and dialogue are exchanged.

National Climatic Data Center – Associated entities

NCDC maintains the US Climate Reference Network datasets amongst a vast number of other climate monitoring products.[ http://www.ncdc.noaa.gov/oa/climate/research/monitoring.html NCDC: Climate Monitoring] The current director of the center is Tom Karl, a lead author on three Intergovernmental Panel on Climate Change science assessments.Fred Pearce|Pearce, Fred, The Climate Files: The Battle for the Truth about Global Warming, (2010) Guardian Books, ISBN 978-0-85265-229-9, p. XVIII.

Everspin – Data Center and Storage

MRAM is used in storage, server and networking applications such as RAID, Network-attached storage|NAS, Storage area network|SAN and Direct-attached storage|DAS applications, as well as rack and blade servers and routers.

CCNA Security – Data Center

Four required exams:

World Data Center

The ‘World Data Centre’ (WDC) system was created to archive and distribute data collected from the observational programmes of the 1957-1958 International Geophysical Year by the International Council of Science (ICSU). The WDCs were funded and maintained by their host countries on behalf of the international science community.

World Data Center

Originally established in the United States, Europe, Soviet Union, and Japan, the WDC system expanded to other countries and to new scientific disciplines. The WDC system included up to 52 Centres in 12 countries. All data held in WDCs were available for the cost of copying and sending the requested information.

World Data Center

At the end of 2008, following the ICSU General Assembly in Maputo (Mozambique), the World Data Centres were reformed and a new ICSU World Data System (WDS) established in 2009 building on the 50-year legacy of the ICSU World Data Centre system (WDC) and the ICSU Federation of Astronomical and Geophysical data-analysis Services.

Data center environmental control

‘Data center environmental control’ is a constructive generic framework for maintaining temperature, humidity, and other physical qualities of air within a specific range in order to allow the equipment housed in a data center to perform optimally throughout its Product lifecycle|lifespan.

Data center environmental control – Air flow

Air flow management addresses the need to improve data center computer cooling efficiency by preventing the recirculation of hot air exhausted from IT equipment and reducing bypass airflow. There are several methods of separating hot and cold airstreams, such as hot/cold aisle containment and in-row cooling units.

Data center environmental control – Air flow

Overheating of data center equipment can result in reduced server performance or equipment damage due to hot exhaust air finding its way into an air inlet. Atmospheric stratification can require setting cooling equipment temperatures lower than recommended. Mixing the cooled air and exhausted air increases refrigeration costs.

Data center environmental control – Temperature

Research has shown, however, that the practice of keeping data centers at or below may be wasting money and energy

Data center environmental control – Rack Hygiene

Blanking plates and other fittings around the edge, top, floor, or the rack direct air intake so that only air from the cold aisle reaches equipment intakes and prevent leakage of exhaust air into the intake area. Fans on the top or rear doors of the cabinet ensure a negative pressure for exhaust air coming out of equipment. Effective airflow management prevents hot spots, which are especially common in the top spaces of a rack, and allows the temperature of cold aisles to be raised.

Data center environmental control – Hot/Cold Aisle Containment

Containment of hot/cold aisles and ducting hot air from cabinets are intended to prevent cool/exhaust air mixing within server rooms. Generally rows of cabinets face each other so that cool air can reach the equipment air intakes at the set temperature point for the room.

Data center environmental control – Hot/Cold Aisle Containment

A more recent addition to the consideration of above floor containment is below floor air flow control. A range of underfloor panels can be fitted within the raised floor plenum to create efficient cold air pathways direct to the raised floor vented tiles.

Data center environmental control – Hot/Cold Aisle Containment

Containment is generally implemented by physical separation of the hot and cold aisles, using blanking panels, PVC curtains or hard panel boards. Containment strategies could differ based on various factors including server tolerance, ambient temperature requirements and leakage from data centers.[ http://heatchain.info/datacenter-energy-containment-hot-aisle-or-cold-aisle/ Data center containment – Hot aisle or cold aisle?]

HP OpenView – HP Software Data Center Automation (DCA)

Formerly products from Opsware and now integrated into the HP portfolio:

HP OpenView – HP Software Data Center Automation (DCA)

* HP Software Server Automation (SA) (formerly Opsware Server Automation System (SAS))

HP OpenView – HP Software Data Center Automation (DCA)

* HP Software Storage Essentials (SE) (HP existing product Storage Essentials has been merged with former Opsware Application Storage Automation System (ASAS))

HP OpenView – HP Software Data Center Automation (DCA)

* HP Software Operations Orchestration (OO) (formerly Opsware Process Automation System (PAS) (formerly [ http://www.networkcomputing.com/data-networking-management/opsware-adds-it-process-automation-with-iconclude-acquisition.php iConclude Orchestrator]))

HP OpenView – HP Software Data Center Automation (DCA)

* HP Software Network Automation (NA) (formerly Opsware Network Automation System NAS))

Sustainable computing – Data center design

Department of Energy estimates that data center facilities consume up to 100 to 200 times more energy than standard office buildings.“Best Practices Guide for Energy-Efficient Data Center Design”, prepared by the National Renewable Energy Laboratory for the U.S

Sustainable computing – Data center design

The U.S. Department of Energy specifies five primary areas on which to focus energy efficient data center design best practices:Koomey, Jonathon. “Growth in data center electricity use 2005 to 2010,” Oakland, CA: Analytics Press. August 1. [ http://www.analyticspress.com/datacenters.html]

Sustainable computing – Data center power

federal government has set a minimum 10% reduction target for data center energy usage by 2011

Network-neutral data center

A ‘network-neutral data center’ (or ‘carrier-neutral data center’) is a data center (or carrier hotel) which allows interconnection between multiple telecommunication carriers and/or Colocation (business)|colocation providers. Network-neutral data centers exist all over the world and vary in size and power.

Network-neutral data center

While some data centers are owned and operated by a Telecommunications service provider|telecommunications or Internet service provider, network-neutral data centers are operated by a third party who has little or no part in providing Internet service to the end-user. This encourages competition and diversity as a Server (computing)|server in a colocation centre can have one provider, multiple providers or only connect back to the headquarters of the company who owns the server.

Network-neutral data center

One benefit of Internet hosting service|hosting in a network-neutral data center is the ability to switch providers without physically moving the server to another location.

Data center infrastructure management

Since DCIM is a broadly used term which covers a wide range of data center management values, each deployment will include a subset of the full DCIM value needed and expected over time.

Data center infrastructure management

With more than 75 vendors in 2014 self-identifying their offerings to be part of the DCIM market segment, the rapid evolution of the DCIM category is leading to the creation of many associated data center performance management and measurement metrics, including industry standard metrics like PUE, CUE and ‘DCeP – Data Center Energy Productivity’ as well as vendor-driven metrics such as ‘PAR4 – Server Power Usage’ and Data center predictive modeling|DCPM – Data Center Predictive Modeling with the intention of providing increasingly cost-effective planning and operations support for certain aspects of the data center and it’s contained devices.

Data center infrastructure management

In general, these specialists can be viewed as enhancements to the DCIM Suite offerings and in most cases can also be used as viable stand-lone solution to a specific set of data center management needs.

Data center infrastructure management

The inefficiencies seen previously by having limited visibility and control at the physical layer of the data center is simply too costly for end-users and vendors alike in the energy-conscious world we live in

Data center infrastructure management

While the physical layer of the data center has historically been viewed as a hardware exercise, there are a number of DCIM Suite and DCIM Specialist SOFTWARE vendors who offer varied DCIM capabilities including one or more of the following; Capacity Planning, high-fidelity visualization, Real-Time Monitoring, Cable/Connectivity management, Environmental/Energy sensors, business analytics (including financial modeling), Process/Change Management and integration well with various types of external management systems and data sources.

Data center infrastructure management

In 2011 some predicted data center management domains would converge across the logical and physical layers. This type of converged management environment will allow enterprises to use fewer resources, eliminate stranded capacity, and manage the coordinated operations of these otherwise independent components.

Data center infrastructure management – Driving factors

According to an IT analyst at Gartner and presented in December 2013, By 2017, DCIM tools will be significantly deployed in over 60% of larger data centers (ref name=Cappuccio /references /

Portable Modular Data Center

The ‘Portable Modular Data Center’ (PMDC) is a portable data center solution built into a standard 20, 40, or 53-foot intermodal container (shipping container) manufactured and marketed by IBM. IBM states that a PMDC cost 30% less to design and build than a traditional data center with cooling equipment.[ http://www.theregister.co.uk/2009/12/07/ibm_data_center_containers/ IBM thinks outside the box with containerized data centers]

Portable Modular Data Center – Portability

The Portable Modular Data Center loaded with computer equipment can be transported using standard shipping methods. The PMDC is weather resistant and insulated, and can be placed in environments like tundra or the desert.[ http://www-935.ibm.com/services/us/its/pdf/sff03002-usen-00_hr.pdf Delivering rapid deployment of a complete, turn-key modular data center to support your unique business objectives]

Modular data center

A ‘modular data center’ system is a portable method of deploying data center capacity. An alternative to the traditional data center, a modular data center can be placed anywhere data capacity is needed.

Modular data center

“DCK Guide To Modular Data Centers: Why Modular?” DataCenterKnowledge.com, Oct

Modular data center

“Cisco Unveils New Containerized Data Center Product,” Web Host Industry Review, May 2, 2011

Modular data center

“HP says prefab data center cuts costs in half,” InfoWorld, July 27, 2010

Modular data center

Modular data centers are a form of the emerging infrastructure convergence (or converged infrastructure) approaches that allow for substantial economies of scale and have been designed with more efficient energy usage in mind, including considerations regarding the external environment

Modular data center

Modular data centers are designed for rapid deployment, energy efficiency and high-density computing to deliver data center capacity at a lower cost than traditional construction methods, and significantly reduce the construction time from years to a matter of months.Worthen, Ben. “Data Centers Boom,” The Wall Street Journal, April 19, 2011. [ http://online.wsj.com/article/SB10001424052748704336504576259180354987332.html]

Modular data center – Examples

*Portable Modular Data Center|IBM Portable Modular Data Center

Raised floor – Telecommunications data center applications

Raised floors available for general purpose use typically do not address the special requirements needed for telecommunications applications.

Raised floor – Telecommunications data center applications

The general types of raised floors in telecommunications data centers include: stringerless, stringered, and structural platforms; and, truss assemblies.

Raised floor – Telecommunications data center applications

* Stringerless raised floors — an array of pedestals that provide the necessary height for routing cables and also serve to support each corner of the floor panels.

Raised floor – Telecommunications data center applications

* Stringered raised floors — a vertical array of steel pedestal assemblies (steel base plate, tubular upright, and a head) uniformly spaced on 2-foot centers and mechanically fastened to the concrete floor.

Raised floor – Telecommunications data center applications

* Structural platforms — members constructed of steel angles or channels that are welded or bolted together to form an integrated platform for supporting equipment.

Raised floor – Telecommunications data center applications

* Truss assemblies — utilizing attachment points to the subfloor to support a truss network on which the floor panels rest. The truss has high lateral strength and transfers lateral loads to the subfloor with less strain than possible with a vertical pedestal assembly.

Raised floor – Telecommunications data center applications

A telecommunications facility may contain continuous lineups of equipment cabinets

Raised floor – Telecommunications data center applications

The data center can be located in remote locations, and is subject to physical and electrical stresses from sources such as fires and from electrical faults.

Raised floor – Telecommunications data center applications

The actual installation should be in accordance with the customer’s practices.[ http://telecom-info.telcordia.com/site-cgi/ido/docs.cgi?ID=SEARCHDOCUMENT=GR-1275 GR-1275-CORE,] Central Office/Network Environment Equipment Installation/Removal Generic Requirements[ http://telecom-info.telcordia.com/site-cgi/ido/docs.cgi?ID=SEARCHDOCUMENT=GR-3160 GR-3160-CORE,] NEBS™ Requirements for Telecommunications Data Center Equipment and Spaces

Google Web Server – Modular container data centers

Since 2005,http://www.theregister.co.uk/2009/04/10/google_data_center_video Google has been moving to a containerized Google Modular Data Center|modular data center. Google filed a patent application for this technology in 2003.

National Snow and Ice Data Center

The ‘National Snow and Ice Data Center’, or ‘NSIDC’, is a United States information and referral center in support of geographical pole|polar and cryosphere|cryospheric research. NSIDC archives and distributes digital and analog snow and ice data and also maintains information about snow|snow cover, avalanches, glaciers, ice sheets, freshwater ice, sea ice, ground ice, permafrost, atmospheric ice, paleoglaciology, and ice cores.

National Snow and Ice Data Center

NSIDC also supports the National Science Foundation through the [http://nsidc.org/acadis Advanced Cooperative Arctic Data and Information Service] (ACADIS), the Exchange for Local Observations and Knowledge of the Arctic|Exchange For Local Observations and Knowledge of the Arctic (ELOKA) and the Antarctic Glaciological Data Center

National Snow and Ice Data Center – History

The World Data Center (WDC) for Glaciology, Boulder, Colorado|Boulder, a data center responsible for archiving all available glaciological information, was established at the American Geographical Society under Dr. William O. Field, Director, in 1957. Between 1971 and 1976 it was operated by the U.S. Geological Survey, Glaciology Project Office, under the direction of Dr. Mark F. Meier.

National Snow and Ice Data Center – History

In 1982, NOAA created the National Snow and Ice Data Center (NSIDC) as a means to expand the WDC holdings and as a place to archive data from some NOAA programs

National Snow and Ice Data Center – Milestones

* ‘1957’: U.S. National Committee for the IGY awards the operation of WDC-A for Glaciology to the American Geographical Society

National Snow and Ice Data Center – Milestones

* ‘1970’: WDC for Glaciology transfers from the American Geographical Society to the U.S. Geological Survey in Tacoma, Washington

National Snow and Ice Data Center – Milestones

* ‘1976’: WDC for Glaciology transfers from the U.S. Geological Survey in Tacoma, Washington to the University of Colorado at Boulder, Colorado under the direction of Roger Barry

National Snow and Ice Data Center – Milestones

* ‘1983’: NSIDC receives grant from NASA for archiving Nimbus 7 passive microwave data

National Snow and Ice Data Center – Milestones

* ‘1990’: NSIDC receives funding from NSF for Arctic System Science (ARCSS) Data Coordination Center (Arctic System Science Data Coordination Center|ADCC)

National Snow and Ice Data Center – Milestones

* ‘1996’: Antarctic Data Coordination Center (ADCC) established with NSF support

National Snow and Ice Data Center – Milestones

* ‘1999’: Antarctic Glaciological Data Center (AGDC) established with NSF support

National Snow and Ice Data Center – Milestones

* ‘2002’: Frozen Ground Data Center established with International Arctic Research Center (IARC) support

National Snow and Ice Data Center – Milestones

* ‘2003’: Full suite of Earth Observing System (EOS) cryospheric sensors (AMSR, GLAS, MODIS) in orbit

National Snow and Ice Data Center – International interactions

International science and data management programs facilitate the free exchange of data and accelerate research aimed at understanding the role of the cryosphere in the Earth system. NSIDC contributes to a number of international programs. Most of these programs, only a few of which are mentioned here, fall under the aegis of the International Council of Scientific Unions (ICSU).

National Snow and Ice Data Center – International interactions

NSIDC scientists participate in International Union of Geophysics and Geodetics (IUGG), International Association of Cryospheric Scientists (IACS), and in activities of the International Permafrost Association (IPA), the Global Digital Sea Ice Data Bank (GDSIDB), and the World Climate Research Programme (WCRP), including Climate and Cryosphere (CliC), Global Energy and Water Cycle Experiment (GEWEX), the Global Climate Observing System (GCOS), and the Global Earth Observation System of Systems (GEOSS)

National Snow and Ice Data Center – Research

Researchers at NSIDC investigate the dynamics of Antarctic ice shelves, new techniques for the remote sensing of snow and freeze/thaw cycle of soils, the role of snow in hydrologic modeling, linkages between changes in sea ice extent and weather patterns, large-scale shifts in polar climate, river and lake ice, and the distribution and characteristics of seasonally and permanently frozen ground. In-house scientists pursue their work as part of the CIRES Cryospheric and Polar Process Division,

International Geophysical Year – World Data Centers

Although the 1932 Polar Year accomplished many of its goals, it fell short on others because of the advance of World War II. In fact, because of the war, much of the data collected and scientific analyses completed during the 1932 Polar Year were lost forever.

International Geophysical Year – World Data Centers

The potential loss of data to war and politics was particularly troubling to the IGY organizing committee. The committee resolved that all observational data shall be available to scientists and scientific institutions in all countries. They felt that without the free exchange of data across international borders, there would be no point in having an IGY.

International Geophysical Year – World Data Centers

In April 1957, just three months before the IGY began, scientists representing the various disciplines of the IGY established the World Data Center system. The United States hosted World Data Center A and the Soviet Union hosted World Data Center B. World Data Center C was subdivided among countries in Western Europe, Australia, and Japan. Today, NOAA hosts seven of the fifteen World Data Centers in the United States.

International Geophysical Year – World Data Centers

Each World Data Center would eventually archive a complete set of IGY data to deter losses prevalent during the International Polar Year of 1932. Each World Data Center was equipped to handle many different data formats, including computer punch cards and tape—the original computer media. In addition, each host country agreed to abide by the organizing committee’s resolution that there should be a free and open exchange of data among nations.

New York City Department of Information Technology and Telecommunications – Data Center Consolidation

The Citywide IT Services (CITIServ) program consolidates the City’s more than 50 separate data centers into a modern, unified, shared services environment.http://www.nyc.gov/html/om/html/2011a/pr064-11.html Press Release: Mayor Bloomberg Opens New Consolidated Data Center to House Technology Infrastructure of more than 40 City Agencieshttp://www.cioinsight.com/c/a/Case-Studies/New-York-Citys-IT-Roadmap-636940/5/ CIO Insight

Software-defined data center

‘Software-defined data center (SDDC)’ is a vision for IT infrastructure that extends virtualization concepts such as abstraction, pooling, and automation to all of the data center’s resources and services to achieve IT as a service (ITaaS).

Software-defined data center

In a software-defined data center, all elements of the infrastructure — networking, storage, CPU and security – are virtualized and delivered as a service. While ITaaS may represent an outcome of SDDC, SDDC is differently cast toward integrators and datacenter builders rather than toward tenants. Software awareness in the infrastructure is not visible to tenants.

Software-defined data center

Because it is a vision with many possible implementation scenarios, SDDC support can be claimed by a wide variety of approaches. Critics see the software-defined data center as a marketing tool and “software-defined hype”, noting this variability.

Software-defined data center

Proponents believe that software will define the data centers of the future and accept the SDDC as a work in progress.

Software-defined data center

Analysts project that at least some software-defined data center components will experience strong market growth in the near future. The software-defined networking market is expected to be valued at about USD $3.7 billion by 2016, compared to USD $360 million in 2013. International Data Corporation|IDC estimates that the Software defined storage|software-defined storage market is poised to expand faster than any other storage market.

Software-defined data center – Description and core components

The software-defined data center encompasses a variety of concepts and data center infrastructure components, and each component can be provisioned, operated, and managed through an application programming interface (API). The core architectural components that comprise the software-defined data center include the following:

Software-defined data center – Description and core components

* Compute virtualization, which is a software implementation of a computer.

Software-defined data center – Description and core components

* Software-defined networking (SDN), which includes network virtualization, is the process of merging hardware and software resources and networking functionality into a software-based virtual network.

Software-defined data center – Description and core components

* Software-defined storage (SDS), which includes storage virtualization, suggests a service interface to provision capacity and SLAs (Service Level Agreements) for storage, including performance and durability.

Software-defined data center – Description and core components

* Management and automation software, enabling an administrator to provision, control, and manage all software-defined data center components.

Software-defined data center – Description and core components

A software-defined data center is not the same thing as a private cloud, since a private cloud only has to offer VM self-service, beneath which it could use traditional provisioning and management. Instead, it imagines the data center that can encompass private, public, and hybrid clouds.

Software-defined data center – Origins and development

Realizing the promise of the software-defined data center could “only begin to happen now,” because until recently data centers lacked the compute, storage, and networking hardware with the capacity to fully accommodate virtualization.

Software-defined data center – Origins and development

Some observers believe that companies began laying the foundation for software-defined data centers with virtualization. Ben Cherian of Midokura considers Amazon Web Services as a catalyst for the move toward software-defined data centers because it

Software-defined data center – Origins and development

convinced the world that the data center could be abstracted into much smaller units and could be treated as disposable pieces of technology, which in turn could be priced as a utility. Vendors watched Amazon closely and saw how this could apply to the data center of the future.

Software-defined data center – Potential impact

the traditionally infrastructure-centric data center, with its focus on ensuring the proper operation of compute, network, and storage elements, into an application or business service focused environment.…The [software-defined data center] purely revolves around application workload demands, allowing business users to deploy and run their applications in the most efficient and SLA compliant manner.

Software-defined data center – Potential impact

The potential of the software-defined data center is that companies will no longer need to rely on specialized hardware or hire consultants to install and program hardware in its specialized language. Rather, IT will define applications and all of the resources they require—including compute, storage, networking, security, and availability—and group all of the required components to create a “logical application.”

Software-defined data center – Potential impact

Commonly cited benefits of software-defined data centers include improved efficiencies from extending virtualization throughout the data center; increased agility from provisioning applications quickly; improved control over application availability and security through policy-based governance; and the flexibility to run new and existing applications in multiple platforms and clouds.

Software-defined data center – Potential impact

In addition, a software-defined data center implementation could reduce a company’s energy usage by enabling servers and other data center hardware to run at decreased power levels or be turned off. Some believe that software-defined data centers improve security by giving organizations more control over their hosted data and security levels, compared to security provided by hosted-cloud providers.

Software-defined data center – Potential impact

The software-defined data center is likely to further drive down prices for data center hardware and challenge traditional hardware vendors to develop new ways to differentiate their products through software and services.

Software-defined data center – Challenges

The concepts of software-defined in general, and software-defined data centers in particular, have been dismissed by some as “nonsense,” “marketecture,” and “software-defined hype.” Some critics believe that only a minority of companies with “completely homogenous IT systems’” already in place, such as Yahoo! and Google, can transition to software-defined data centers.

Software-defined data center – Challenges

According to some observers, software-defined data centers won’t necessarily eliminate challenges that relate to handling the differences between development and production environments; managing a mix of legacy and new applications; or delivering service-level agreements (SLAs).

Software-defined data center – Challenges

Software-defined networking is seen as essential to the software-defined data center, but it is also considered to be the “least mature technology” required to enable the software-defined data center. However, a number of companies, including VMware,[http://www.cypherpath.com Cypherpath Inc.], Arista Networks, Cisco, and Microsoft, are working to enable virtual networks that are easily provisioned, extended, and moved across existing physical networks.

Software-defined data center – Challenges

[https://wiki.openstack.org/wiki/Neutron Neutron], the networking component of the open-source OpenStack project, is considered an important piece of the standards puzzle and is expected to play a key role in the evolution of the software-defined data center

Software-defined data center – Challenges

The software-defined data center approach will force IT organizations to adapt. Architecting software-defined environments requires rethinking many IT processes—including automation, metering, and billing—and executing service delivery, service activation, and service assurance.

Software-defined data center – Challenges

A widespread transition to the SDDC could take years:

Software-defined data center – Challenges

Enterprise IT will have to become truly business-focused, automatically placing application workloads where they can be best processed. We anticipate that it will take about a decade until the SDD becomes a reality. However, each step of the journey will lead to efficiency gains and make the IT organization more and more service oriented.

Software-defined data center – Current status

Other vendors are developing components and standards that enable the software-defined data center

Software-defined data center – Current status

Large-scale service providers such as Amazon and Savvis, which could potentially benefit from improved efficiencies through automation, are considered to be the organizations that are most likely to deploy full-scale software-defined data center implementations.

National Space Science Data Center

The ‘National Space Science Data Center’ serves as the permanent archive for NASA space science mission data

National Space Science Data Center

NSSDC supports active space physics and astrophysics researchers. Web-based services allow the NSSDC to support the general public. This support is in the form of information about spacecraft and access to digital versions of selected imagery. NSSDC also

National Space Science Data Center

provides access to portions of their database contains information about data archived at NSSDC (and, in some cases, other facilities), the spacecraft which generate space science data and experiments which generate space science data. NSSDC services also included are data management standards and technologies.

National Space Science Data Center

NSSDC is part of the Solar System Exploration Data Services Office (SSEDSO) in the Solar System Exploration Division at NASA’s Goddard Space Flight Center. NSSDC is sponsored by the Heliophysics Division of NASA’s Science Mission Directorate. NSSDC acts in concert with various NASA discipline data systems in providing certain data and services.

National Space Science Data Center – Overview

NSSDC was first established at Goddard Space Flight Center in 1966

National Space Science Data Center – Astrophysics

Data Services contains data and mission information: The Multiwavelength Milky Way, the Multimedia Catalog and the NSSDC Photo Gallery.

National Space Science Data Center – Astrophysics

Flight Mission Information contains lists of flight missions and information about them; this is where the NSSDC Master Catalog is along with mission-specific access. A graphical interface to mission information is in this area as well.

National Space Science Data Center – Astrophysics

Related Information Services have detailed information about data held at NSSDC via the Master Catalog, NSSDC Lunar and Planetary Science, and NSSDC Heliophysics.

National Space Science Data Center – Astrophysics

There are also NASA Astrophysics Data Archive/Service Centers. These include HEASARC (High Energy Astrophysics Science Archive Research Center), IRSA (Infrared Science Archives), LAMBDA (Legacy Archive for Microwave Background Data Analysis), MAST (Barbara A. Mikulski Archive for Space Telescopes)

National Space Science Data Center – Heliophysics

Heliophysics contains data and information services, data archive, and service centers. Information also includes the Heliophysics Virtual Observatories, using the Space Physics Archive Search and Extract|SPASE data model to describe the resources.

National Space Science Data Center – Master Catalog

The NSSDC Master Catalog is available for the queries pertaining to information about data that are archived at the NSSDC as well as for supplemental information via the following query mechanisms:

National Space Science Data Center – Master Catalog

*Spacecraft Query. This interface allows queries to our database of orbital, suborbital, and interplanetary spacecraft.

National Space Science Data Center – Master Catalog

*Experiment Query. This interface allows queries for information about scientific experiments that flew on-board various space missions.

National Space Science Data Center – Master Catalog

*Data Collection Query. This interface allows queries for data that are tracked by NSSDC, primarily those that are currently archived here.

National Space Science Data Center – Master Catalog

*Personnel Query. This interface allows queries for locator information for personnel that were associated with various missions and/or data collections submitted to NSSDC.

National Space Science Data Center – Master Catalog

*Publication Query. This interface allows queries information about publications that are relevant to the data NSSDC archives or to the experiments and/or missions that accumulated the data. The publications so captured are not intended to be comprehensive bibliographies.

National Space Science Data Center – Master Catalog

*Lunar and Planetary Map Query. This interface allows queries of the lunar and planetary maps that NSSDC currently has in stock.

National Space Science Data Center – Master Catalog

*New and Updated Data Query. This interface allows queries for those data collections for which the NSSDC has recently acquired new data, either additions to existing collections or entirely new collections.

National Space Science Data Center – Master Catalog

*Lunar and Planetary Events Query. This interface allows queries for events that have occurred which are related to the exploration of the Moon and the Solar System.

Tokyo Game Show – Cloud/data center pavilion

The Cloud/Data Center is dedicated to improving infrastructure and environment of social and network games.

Global surveillance – Infiltration of commercial data centers

In contrast to the ‘PRISM (surveillance program)|PRISM’ surveillance program, which is a front-door method of access that is nominally approved by the United States Foreign Intelligence Surveillance Court|FISA court, the ‘Muscular (surveillance program)|MUSCULAR’ surveillance program is noted to be unusually aggressive in its usage of unorthodox hacking methods to infiltrate Yahoo! and Google data centers around the world

Distributed Active Archive Center – List of data centers and data specialization

* Alaska Satellite Facility (ASF): Synthetic Aperture Radar (SAR) data, sea ice, polar processes, geophysics.

Distributed Active Archive Center – List of data centers and data specialization

* [http://cddis.gsfc.nasa.gov/ Crustal Dynamics Data Information System (CDDIS)]: Space geodesy.

Distributed Active Archive Center – List of data centers and data specialization

* [http://ghrc.nsstc.nasa.gov/ Global Hydrology Resource Center (GHRC)]: hydrologic cycle, severe weather interactions, lightning, atmospheric convection.

Distributed Active Archive Center – List of data centers and data specialization

* [http://lpdaac.usgs.gov/ Land Processes DAAC (LP DAAC)]: surface reflectance, land cover, vegetation indices.

Distributed Active Archive Center – List of data centers and data specialization

* [http://ladsweb.nascom.nasa.gov/ Level 1 Atmosphere Archive and Distribution System (MODAPS LAADS)]: radiance, atmosphere.

Distributed Active Archive Center – List of data centers and data specialization

* [http://eosweb.larc.nasa.gov/ NASA Langley Research Center Atmospheric Science Data Center (LaRC ASDC)]: radiation budget, clouds, aerosols, tropospheric chemistry.

Distributed Active Archive Center – List of data centers and data specialization

* [http://oceancolor.gsfc.nasa.gov/ Ocean Biology Processing Group]: ocean biology, ocean color, ocean biogeochemistry, sea surface temperature.

Distributed Active Archive Center – List of data centers and data specialization

* [http://podaac.jpl.nasa.gov/ Physical Oceanography DAAC (PO DAAC)]: sea surface temperature, ocean winds, circulation and currents, topography and gravity.

Distributed Active Archive Center – List of data centers and data specialization

* [http://sedac.ciesin.columbia.edu/ Socioeconomic Data and Applications Data Center (SEDAC)]: human interactions, land use, environmental sustainability, geospatial data, multilateral environmental agreements.

Data-center – Requirements for modern data centers

The topology proposed in this document is intended to be applicable to any size data center.http://www.tiaonline.org/standards/

Data-center – Requirements for modern data centers

They may be applied to data center spaces housing data processing or Information Technology (IT) equipment

Data-center – Requirements for modern data centers

Gartner: Virtualization Disrupts Server Vendors, Data Center Knowledge, December 2, 2008 [http://www.datacenterknowledge.com/archives/2008/12/02/gartner-virtualization-disrupts-server-vendors/]

Data-center – Data center tiers

Another consideration is the placement of the data center in a subterranean context, for data security as well as environmental considerations such as cooling requirements.A ConnectKentucky article mentioning Stone Mountain Data Center Complex

Data-center – Data center tiers

Whilst no down-time is ideal, the tier system allows for unavailability of services as listed below over a period of one year (525,600 minutes):

Data-center – Data center tiers

The Uptime Institute also classifies the tiers in different categories: design documents, constructed facility, operational sustainability

March of Dimes – Perinatal Data Center

The March of Dimes Perinatal Data Center includes the PeriStats Web site, which provides free access to U.S., state, county, and city maternal and infant health data.

Data center predictive modeling

‘Data center predictive modeling’ (DCPM) is the ability to forecast the performance of a data center into the future, be it its energy use, energy efficiency, performance of the myriad pieces of equipment, even cost.

Data center predictive modeling

An important part of forecasting data center performance is the use of computational fluid dynamics (CFD) to quantify the airflow and temperatures that would occur if physical changes were made to the data center space. The use of CFD moves DCPM from a probabilistic type of forecasting to a physics-based one.

Data center predictive modeling

The term DCPM has been in use since June 2011[http://www.itworld.com/data-centerservers/178363/romonet-brings-predictive-data-center-tool-us IT World, ‘Romonet brings predictive data center tool to US’, June 28th, 2011] and was adopted by Romonet to differentiate DCPM from data center infrastructure management (DCIM) which only tracks the present performance of the elements of a data center.[http://www.altaterra.net/members/blog_view.asp?id=288668tag=DCPM Altaterra, ‘Zen and the Art of Data Center Greening (and Energy Efficiency)’, June 28th, 2011]

Data center predictive modeling

Another example of the same technology was presented in Russia[http://omega.sp.susu.ac.ru/books/conference/PaVT2013/talks/Rumyantsev.pdf ‘Load Prediction for HPC Energy Efficiency Improvement (in Russian)’] by Institute of Applied Mathematical Research, Karelian Research Centre, Russian Academy of Sciences. The technology is developed since 2011 under support of FASIE and RFBR.

National Geophysical Data Center

The ‘National Geophysical Data Center’ (NGDC) provides scientific stewardship, products and services for geophysical data describing the solid earth, ocean|marine, and solar-terrestrial natural environment|environment, as well as earth observations from outer space|space.

National Geophysical Data Center – Location and controlling bodies

The NGDC, located in Boulder, Colorado, is a part of the US Department of Commerce (USDOC), National Oceanic Atmospheric Administration (NOAA), National Environmental Satellite, Data and Information Service (NESDIS). They are one of three NOAA National Data Centers (NNDC).

National Geophysical Data Center – Mission

The NOAA NESDIS mission is to provide and ensure timely access to global environmental data from satellites and other sources to promote, protect, and enhance the U.S.’s economy, security, environment, and quality of life. To fulfill its responsibilities NESDIS acquires and manages the U.S.’s operational environmental satellites, provides data and information services, and conducts related research.

National Geophysical Data Center – Data holdings

NGDC’s data holdings currently contain more than 300 Digital data|digital and analog (signal)|analog databases, some of which are very large. As technology advances, so does the search for more efficient ways of preserving these data.

National Geophysical Data Center – Data contributors

NGDC works closely with contributors of scientific data to prepare documented, reliable data sets. They welcome cooperative projects with other government agencies, nonprofit organizations, and universities, and encourage data exchange.

National Geophysical Data Center – Data Users

* universities and other educational facilities

National Geophysical Data Center – Data Users

* foreign governments, industry, and academia

National Geophysical Data Center – Data Users

* publishers and other mass media

National Geophysical Data Center – Data Management

The Data Center continually develops data management programs that reflect the changing world of geophysics.

Arctic Policy of the United States – National Snow and Ice Data Center

**NASA’s Operation Icebridge program works with the NCIDC to monitor the Acrtic region. The NASA aircract missions include maping surface and bedrock topography, determining ice/snow thickness and analyzing sea ice distribution. The areas that are monitored in the region include, but are not limited to, coastal Antarctica, interior Antarctica, Greenland and southeast Alaskan glaciers.

Arctic Policy of the United States – National Snow and Ice Data Center

*Roger G. Barry Resource Office for Cryospheric Studies (ROCS)

Backup battery – Telecommunications networks and data centers

A valve-regulated lead-acid battery (VRLA battery|VRLA) is a battery type that is popular in telecommunications network environments as a reliable backup power source. VRLA batteries are used in the outside plant at locations such as Controlled Environmental Vaults (CEVs), Electronic Equipment Enclosures (EEEs), and huts, and in uncontrolled structures such as cabinets.

Backup battery – Telecommunications networks and data centers

[http://telecom-info.telcordia.com/site-cgi/ido/docs.cgi?ID=SEARCHDOCUMENT=GR-4228 GR-4228, VRLA Battery String Certification Levels Based on Requirements for Safety and Performance], is a new industry-approved set of VRLA requirements that provides a three-level compliance system

Backup battery – Telecommunications networks and data centers

For a VRLA, the quality system employed by the manufacturer is an important key to the overall reliability of it

Dulles Technology Corridor – Internet infrastructure and data centers

In 2013, as much as 70% of the world’s Internet traffic travelled through data centers in Loudoun County

Dulles Technology Corridor – Internet infrastructure and data centers

Wikimedia Foundation (parent of Wikipedia) has its primary data center in the corridor

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Cross-Docking

Third-party logistics – Definition

According to the Council of Supply Chain Management Professionals, 3PL is defined as a firm that provides multiple logistics services for use by customers. Preferably, these services are integrated, or bundled together, by the provider. Among the services 3PLs provide are transportation, warehousing, cross-docking, inventory management, packaging, and freight forwarding.

Third-party logistics – Types of 3PL providers

* Service Developer: this type of 3PL provider will offer their customers advanced value-added services such as: tracking and tracing, cross-docking, specific packaging, or providing a unique security system. A solid IT foundation and a focus on economies of scale and scope will enable this type of 3PL provider to perform these types of tasks.

Cross docking

‘Cross-docking’ is a practice in logistics of unloading materials from an incoming semi-trailer truck or railroad car and loading these materials directly into outbound trucks, trailers, or rail cars, with little or no storage in between. This may be done to change type of conveyance, to sort material intended for different destinations, or to combine material from different origins into transport vehicles (or containers) with the same, or similar destination.

Cross docking

The US military began utilizing cross-dock operations in the 1950s. Wal-Mart began utilizing cross-docking in the retail sector in the late 1980s.

Cross docking

In the LTL trucking industry, cross-docking is done by moving cargo from one transport vehicle directly into another, with minimal or no warehousing. In retail practice, cross-docking operations may utilize staging areas where inbound materials are sorted, consolidated, and stored until the outbound shipment is complete and ready to ship.

Cross docking – Factors influencing the use of retail crossdocks

* Cross-docking is dependent on continuous communication between suppliers, distribution centers, and all points of sale.

Cross-docking

‘Cross-docking’ is a practice in the logistics of unloading materials from an incoming semi-trailer truck or railroad car and loading these materials directly into outbound trucks, trailers, or rail cars, with little or no storage in between. This may be done to change the type of conveyance, to sort material intended for different destinations, or to combine material from different origins into transport vehicles (or containers) with the same destination or similar destinations.

Cross-docking

Cross-dock operations were first pioneered in the US trucking industry in the 1930s, and have been in continuous use in less-than-truckload (LTL) operations ever since. The US military began using cross-docking operations in the 1950s. Wal-Mart began using cross-docking in the retail sector in the late 1980s.

Cross-docking

In the LTL trucking industry, cross-docking is done by moving cargo from one transport vehicle directly onto another, with minimal or no warehousing. In retail practice, cross-docking operations may utilize staging areas where inbound materials are sorted, consolidated, and stored until the outbound shipment is complete and ready to ship.

Cross-docking – Typical applications

Retail cross-dock example: using cross-docking, Wal-Mart was able to effectively leverage its logistical volume into a core strategic competency.

Cross-docking – Factors influencing the use of retail cross-docks

* Cross-docking depends on continuous communication between suppliers, distribution centers, and all points of sale

For More Information, Visit:

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Country Code

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Country Code

Telecommunications in East Timor – Country code

Following Indonesia’s withdrawal from East Timor in 1999, the telecommunications infrastructure was destroyed in the ensuing violence, and Telkom Indonesia ceased to provide services. A new country code (670) was allocated to East Timor by the International Telecommunication Union, but international access often remained severely limited.

Telecommunications in East Timor – Country code

A complicating factor has been the fact that 670 was previously used by the Northern Marianas, with many carriers not aware that the code is now used by East Timor. (The Northern Marianas, as part of the North American Numbering Plan, now use the country code 1 and the area code 670.)

ISO 6346 – Country Code (Optional)

The country code consists of two capital letters of the Latin alphabet as described in ISO 3166. It indicates the country where the code is registered not the nationality of the owner or operator of the container. The letters of the code shall not be less than 100 mm high.

Satellite phone – Virtual country codes

Satellite phones are usually issued with numbers in a special List of country calling codes|country calling code.

Satellite phone – Virtual country codes

Inmarsat satellite phones are issued with codes +870. In the past additional country codes have been allocated to different satellites but the codes +871 to +874 have been phased out at the end of 2008 leaving Inmarsat users with the same country code regardless of which satellite their terminal is registered with.

Satellite phone – Virtual country codes

Low earth orbit systems including some of the defunct ones have been allocated number ranges in the International Telecommunications Union’s Global Mobile Satellite System virtual country code +881. Iridium satellite phones are issued with codes +881 6 and +881 7. Globalstar, although allocated +881 8 and +881 9 use North American Numbering Plan|U.S. telephone numbers except for service resellers located in Brazil which use the +881 range.

Satellite phone – Virtual country codes

Smaller regional satellite phone networks are allocated numbers in the +882 code designated for International Networks (country code)|international networks which is not used exclusively for satellite phone networks.

Vu+ – Country codes

* VU is the country code of Vanuatu

Vu+ – Country codes

* .vu is Vanuatu’s country code top-level domain

Area code – Country code

By convention, international telephone numbers are indicated by prefixing the country code with a plus sign (+), which is meant to indicate that the subscriber must dial the international dialing prefix in the country from which the call is placed

International calling code – Locations with no country code

In Telecommunications in Antarctica|Antarctica, dialing is dependent on the parent country of each base:

Top level domain – Internationalized country code TLDs

An internationalized country code top-level domain (IDN ccTLD) is a top-level domain with a specially encoded domain name that is displayed in an end user application, such as a web browser, in its language-native script or alphabet, such as the Arabic alphabet, or a non-alphabetic writing system, such as Chinese characters. IDN ccTLDs are an application of the internationalized domain name (IDN) system to top-level Internet domains assigned to countries, or independent geographic regions.

Top level domain – Internationalized country code TLDs

ICANN started to accept applications for IDN ccTLDs in November 2009, and installed the first set into the Domain Names System in May 2010. The first set was a group of Arabic names for the countries of Egypt, Saudi Arabia, and the United Arab Emirates. By May 2010, 21 countries had submitted applications to ICANN, representing 11 scripts.

Country code top-level domain

A ‘country code top-level domain’ (‘ccTLD’) is an Internet top-level domain generally used or reserved for a country, a sovereign state, or a dependent territory.

Country code top-level domain

All ASCII ccTLD identifiers are two letters long, and all two-letter top-level domains are ccTLDs. In 2010, the Internet Assigned Numbers Authority (IANA) began implementing internationalized country code TLDs, consisting of language-native characters when displayed in an end-user application. Creation and delegation of ccTLDs is described in RFC 1591, corresponding to ISO 3166-1 ISO 3166-1 alpha-2|alpha-2 country codes.

Country code top-level domain – Delegation and management

IANA is responsible for determining an appropriate trustee for each ccTLD

Country code top-level domain – Relation to ISO 3166-1

cquote2|The IANA is not in the business of deciding what is and what is not a country. The selection of the ISO 3166 list as a basis for country code top-level domain names was made with the knowledge that ISO has a procedure for determining which entities should be and should not be on that list.|Jon Postel|RFC 1591

Country code top-level domain – Unused ISO 3166-1 codes

Almost all current ISO 3166-1 codes have been assigned and do exist in DNS.

Country code top-level domain – Unused ISO 3166-1 codes

However, some of these are effectively unused. In particular, the ccTLDs for the Norwegian dependency Bouvet Island (.bv|bv) and the designation Svalbard and Jan Mayen (.sj|sj) do exist in DNS, but no subdomains have been assigned, and it is Norid policy not to assign any at present. Two France|French territories, .bl|bl (Saint Barthélemy) and .mf|mf (Saint Martin (France)|Saint Martin), await local assignment by France’s government.

Country code top-level domain – Unused ISO 3166-1 codes

The code .eh|eh, although eligible as ccTLD for Western Sahara, has never been assigned and does not exist in Domain Name System|DNS. Only one subdomain is still registered in .gb|gb (ISO 3166-1 for the United Kingdom) and no new registrations are being accepted for it. Sites in the United Kingdom generally use .uk|uk (see below).

Country code top-level domain – Unused ISO 3166-1 codes

The former .um ccTLD for the United States Minor Outlying Islands|U.S. Minor Outlying Islands was removed in April 2008. Under RFC 1591 rules .um is eligible as a ccTLD on request by the relevant governmental agency and local Internet user community.

Country code top-level domain – ASCII ccTLDs not in ISO 3166-1

Several ASCII ccTLDs are in use that are not ISO 3166-1 two-letter codes. Some of these codes were specified in older versions of the ISO list.

Country code top-level domain – ASCII ccTLDs not in ISO 3166-1

* .uk|uk (United Kingdom): The ISO 3166-1 code for the United Kingdom is GB. However, the JANET network had already selected uk as a top-level identifier for its pre-existing JANET NRS|Name Registration Scheme, and this was incorporated into the DNS root. .gb|gb was assigned with the intention of a transition, but this never occurred and the use of uk is now entrenched.

Country code top-level domain – ASCII ccTLDs not in ISO 3166-1

* .su|su This obsolete ISO 3166 code for the Soviet Union was assigned when the Soviet Union was still extant; moreover, new su registrations are accepted.

Country code top-level domain – ASCII ccTLDs not in ISO 3166-1

* .ac|ac (Ascension Island): This code is a vestige of Internet Assigned Numbers Authority|IANA’s decision in 1996 to allow the use of codes reserved in the ISO 3166-1 alpha-2 reserve list for use by the Universal Postal Union. The decision was later reversed, with Ascension Island now the sole outlier. (Three other ccTLDs, .gg|gg (Guernsey), .im|im (Isle of Man) and .je|je (Jersey) also fell under this category from 1996 until they received corresponding ISO 3166 codes in March 2006.)

Country code top-level domain – ASCII ccTLDs not in ISO 3166-1

* .eu|eu (European Union): On September 25, 2000, ICANN decided to allow the use of any two-letter code in the ISO 3166-1 reserve list that is reserved for all purposes

Country code top-level domain – ASCII ccTLDs not in ISO 3166-1

* .tp|tp (the previous ISO 3166-1 code for East Timor): Being phased out in favor of .tl|tl since 2005.

Country code top-level domain – Historical ccTLDs

There are two ccTLDs that have been deleted after the corresponding 2-letter code was withdrawn from ISO 3166-1: .cs|cs (for Czechoslovakia) and .zr|zr (for Zaire)

Country code top-level domain – Historical ccTLDs

The historical country codes .dd|dd for the German Democratic Republic and yd for South Yemen were eligible for a ccTLD, but not allocated; see also .de|de and .ye|ye.

Country code top-level domain – Historical ccTLDs

The temporary reassignment of country code cs (Serbia and Montenegro) until its split into .rs|rs and .me|me (Serbia and Montenegro, respectively) led to some controversies about the stability of ISO 3166-1 country codes, resulting in a second edition of ISO 3166-1 in 2007 with a guarantee that retired codes will not be reassigned for at least 50 years, and the replacement of RFC 3066 by RFC 4646 for country codes used in language tags in 2006.

Country code top-level domain – Historical ccTLDs

The previous ISO 3166-1 code for Yugoslavia, YU, was removed by ISO on 2003-07-23, but the .yu|yu ccTLD remained in operation. Finally, after a two-year transition to Serbian .rs|rs and Montenegrin .me|me, the .yu domain was phased out in March 2010.

Country code top-level domain – Historical ccTLDs

Australia was originally assigned the .au#Historic second-level domains|oz country code, which was later changed to .au|au with the .oz domains moved to .oz.au.

Country code top-level domain – Internationalized ccTLDs

ICANN requires all potential international TLDs to use at least one letter that does not resemble a Latin letter, or have at least three letters, in an effort to avoid IDN homograph attacks. However, this does not protect against homograph attacks involving two non-Latin alphabets having letters often represented by similar glyphs, such as the Cyrillic and Greek alphabets, for example.

Country code top-level domain – Unconventional usage

Lenient registration restrictions on certain ccTLDs have resulted in various domain hacks

Country code top-level domain – Unconventional usage

Some ccTLDs may also be used for typosquatting. The domain cm of Cameroon has generated interest due to the possibility that people might miss typing the letter o for sites in the com.

Country code top-level domain – Commercial usage

Some of the world’s smallest countries and non-sovereign or colonial entities with their own country codes have opened their TLDs for worldwide commercial use.

Chapman code – Country codes

*ALL All countries

Thuraya – Virtual country code

Thuraya’s List of country calling codes|country calling code is +882 16, which is part of the ITU-T International Networks (country code)|International Networks numbering group. Thuraya is not part of the +881 List of country calling codes|country calling code numbering group as this is allocated by ITU-T for networks in the Global Mobile Satellite System, of which Thuraya is not a part, being a regional rather than a global system.

.jp – Internationalized country code top-level domain

Japan has considered registering an internationalized country code top-level domain, ‘.??’.[http://jprs.co.jp/en/notice/dotnippon.html About ‘.??’]

.jp – Internationalized country code top-level domain

Japan has yet not (2013) introduced internationalized domain names.

.in – Internationalised domain names and country codes

India plans to introduce internationalised domain names, that is domain names in 22 local languages used in India. These internationalised domain names will be used together with seven new top domains for India.

.in – Internationalised domain names and country codes

* .???? (Devanagari), became available on 27 Aug 2014

Postcode – Country code prefixes

ISO 3166-1 alpha-2 country codes were recommended to be used in conjunction with postal codes starting in 1994, but they have not become widely used. The European Committee for Standardization recommends use of ISO Alpha-2 codes for international postcodes and a UPU guide on international addressing states that administrations may recommend the use of ISO Alpha-2 codes.

Postcode – Country code prefixes

Andorra, Azerbaijan, Barbados, Ecuador and Saint Vincent and the Grenadines use the ISO 3166-1 alpha-2 as a prefix in their postal codes.

Postcode – Country code prefixes

In some countries (such as those of continental Europe, where a numeric postcode format of four or five digits is commonly used) the numeric postal code is sometimes prefixed with a country code when sending international mail to that country.

Mobile country codes

A ‘mobile country code’ (MCC) is used in combination with a ‘mobile network code’ (MNC) (also known as a MCC / MNC tuple) to uniquely identify a mobile phone operator (carrier) using the GSM, UMTS, LTE (telecommunication)|LTE, and Integrated Digital Enhanced Network|iDEN public land mobile networks as well as some CDMA, Terrestrial Trunked Radio|TETRA, and satellite mobile networks.

Mobile country codes

The following tables contain the complete list of mobile phone operators. Country information, including ISO 3166-1 alpha-2|ISO 3166-1 country codes is provided for completeness.

Mobile country codes

The ITU-T Recommendation E.212 defines mobile country codes as well as mobile network codes, and you may want to visit that list if technical correctness is a concern (for example: MNC of 001 is not the same as MNC of 01), or if you are looking for a normative reference. Note, though, that the official list may not contain disputed territories such as Abkhazia or Kosovo or additional details about bands or operator names.

Mobile country codes – Austria – AT[http://www.rtr.at/en/tk/SKP RTR Special communication parameters]

(see :de:Österreichischer Mobilfunkmarkt|Österreichischer Mobilfunkmarkt and :de:Telefonvorwahl (Österreich)#Mobilfunknetze|Telefonvorwahlen der österreichischen Mobilfunknetze for further information)

Mobile country codes – Norway – NO

Official Norwegian allocations:http://www.npt.no/npt/numsys/E.212.pdf

Global Mobile Satellite System – Satellite numbers outside the GMSS country code

Inmarsat is a satellite-based communications provider, but it is primarily a maritime service and is not generally considered part of the GMSS.

Global Mobile Satellite System – Satellite numbers outside the GMSS country code

Globalstar usually allocates subscribers with a local number in the country they are based rather than using their GMSS country code.

Global Mobile Satellite System – Satellite numbers outside the GMSS country code

Iridium Communications Inc.|Iridium also uses an Arizona-based access number to call Iridium phones for those unwilling or unable to call the usually expensive GMSS number directly.

Global Mobile Satellite System – Satellite numbers outside the GMSS country code

Thuraya has been assigned +882-16 which is part of the +882 country code.

The Country Code

‘The Country Code’, ‘The Countryside Code’ and ‘The Scottish Outdoor Access Code’ are sets of rules for visitors to rural, and especially agricultural, regions of the United Kingdom. The Country Code dates back to the 1930s; the Countryside Code replaced it in 2004.

The Country Code – The original rules

The Country Code evolved from the work of various organisations and had several different versions from the 1930s. The most widely accepted version of The Country Code was published in 1981 by Countryside Agency|The Countryside Commission:

The Country Code – The original rules

*Enjoy the countryside and respect its life and work

The Country Code – The original rules

*Guard against all risk of fire

The Country Code – The original rules

*Keep to public paths across farmland

The Country Code – The original rules

*Use gates and stiles to cross fences, hedges and walls

The Country Code – The original rules

*Take your litter home

The Country Code – The original rules

*Take special care on country roads

The Country Code – The original rules

*Make no unnecessary noise

The Country Code – The original rules

In the 1960s and 70s the Country Code was publicised by several public information films on television.

The Country Code – The Countryside Code

In 2004 The Country Code was revised and relaunched as ‘The Countryside Code’ (‘Côd Cefn Gwlad’ in Welsh) to reflect the introduction of new open access rights and changes in society over the preceding years. The revised Code was produced through a partnership between the Countryside Agency and the Countryside Council for Wales:

The Country Code – The Countryside Code

*Leave gates and property as you find them

The Country Code – The Countryside Code

*Protect plants and animals, and take your litter home

The Country Code – The Scottish Outdoor Access Code

In Scotland, where there is a more general right of access, Scottish Natural Heritage developed ‘The Scottish Outdoor Access Code’:

The Country Code – The Scottish Outdoor Access Code

*Take responsibility for your own actions

The Country Code – The Scottish Outdoor Access Code

*Take extra care if you are organising a group, an event or running a business

The Country Code – The Scottish Outdoor Access Code

The Scottish Outdoor Access Code was approved in draft form by the Scottish Parliament in July 2003 following the passing of the Land Reform (Scotland) Act of the same year, and was accepted in February 2005.

The Country Code – The Scottish Outdoor Access Code

For both The Countryside Code and The Scottish Outdoor Access Code, there is corresponding advice for land managers. The constituent points of each code are described in more detail in full publications.

List of FIFA country codes

FIFA assigns a three-letter ‘country code’ (more properly termed a ‘trigramme’ or ‘trigram’) to each of its member and non-member countries

Inmarsat – Country codes

The permanent country calling codes|telephone country code for calling Inmarsat destinations is:

Inmarsat – Country codes

The 870 number is an automatic locator; it is not necessary to know to which satellite the destination Inmarsat terminal is logged-in. SNAC is now usable by all Inmarsat services.

Inmarsat – Country codes

The other four country codes corresponded to the areas that Inmarsat satellites cover (normally one satellite per area). These areas were commonly called Ocean Regions. With the advent of SNAC on 870, the older country codes were no longer needed. They were formally phased out on 31 December 2008 but may still be routed by some regional carriers.

List of FIPS country codes

This is a list of ‘Federal Information Processing Standard|FIPS 10-4’ List of FIPS region codes|country codes for Countries, Dependencies, Areas of Special Sovereignty, and Their Principal Administrative Divisions.

List of FIPS country codes

The two-letter country codes were used by the US government for geographical data processing in many publications, such as the CIA World Factbook. The standard is also known as DAFIF 0413 ed 7 Amdt. No. 3 (Nov 2003) and as DIA 65-18 (Defense Intelligence Agency, 1994, Geopolitical Data Elements and Related Features).

List of FIPS country codes

The FIPS standard includes both the codes for independent countries (similar but sometimes incompatible with the ISO 3166-1 alpha-2 standard) and the codes for top-level subdivision of the countries (similar to but usually incompatible with the ISO 3166-2 standard). The ISO 3166 codes are used by the United Nations and for Internet top-level country code domains.

List of FIPS country codes

On September 2, 2008, FIPS 10-4 was one of ten standards withdrawn by NIST as a Federal Information Processing Standard. It was replaced in the U.S. Government by the Geopolitical Entities, Names, and Codes (GENC), which is based on ISO 3166.

List of FIPS country codes – Resources

Updates to previous version of the standard (before FIPS-10 was withdrawn in September 2008) are at:

List of FIPS country codes – Resources

Updates to the standard since September 2008 are at:

List of FIPS country codes – Resources

* FIPS PUB 10-4: Federal Information Processing Standard 10-4: [http://earth-info.nga.mil/gns/html/FIPS10-4_match.pdf Countries, Dependencies, Areas of Special Sovereignty, and Their Principal Administrative Divisions], April 1995

List of FIPS country codes – Resources

* DIA 65-18: Defense Intelligence Agency, Geopolitical Data Elements and Related Features, 1994

International Networks (country code)

‘International Networks’ is the name given by the International Telecommunications Union (ITU) to List of country calling codes|country calling codes 882 and 883, and serves as a catch-all for telephone services not dedicated to a single country. Satellite telephone carriers, especially those with worldwide service, are allocated within the Global Mobile Satellite System (GMSS), country code 881, with the exception of non-terrestrial Inmarsat, country code 870.

International Networks (country code)

As in the other such shared country codes, carriers are allocated number space within this code space plus their identification code (two-digit number in 882 code space, three or four digit number in 883 code space). The phone number for a subscriber of such a service will start with ‘+882’/’+883’ followed by the carrier code.

International Networks (country code) – Active

In the +882-99 block, two numbering spaces are currently colliding: The numbering area has officially been assigned to Telenor but prior to this assignment, e164.org has started to assign unofficial numbers within that numbering area.[http://www.e164.org/about.php www.e164.org/about.php].

International Networks (country code) – Inactive

The following codes have been previously assigned by the ITU but were not used as of 2007:

List of IOC country codes

The International Olympic Committee (‘IOC’) uses three-letter abbreviation ‘country codes’http://www.olympic.org/Documents/Commissions_PDFfiles/Olympic_Solidarity/2011_report_Moving_Forward.pdf to refer to each group of athletes that participate in the Olympic Games

List of IOC country codes

Several of the IOC codes are different from the standard ISO 3166-1 alpha-3 codes. Other sporting organisations, such as FIFA, use similar country codes to refer to their respective teams, but with some differences. Still others, such as the Commonwealth Games Federation or Association of Tennis Professionals, use the IOC list verbatim.

List of IOC country codes – History

The 1956 Winter Olympics and 1960 Summer Olympics were the first Games to feature Initials of Nations to refer to each NOC in the published official reports

List of IOC country codes – History

In addition to this list of over 200 NOCs, the participation of National Paralympic Committees (NPCs) at the Paralympic Games requires standardised IOC codes, such as Macau and the Faroe Islands, coded MAC and FRO respectively.[http://www.london2012.com/paralympics/country/faroe-islands/ Faroe Islands][http://www.london2012.com/paralympics/country/macao/ Macau, China]

List of IOC country codes – Current NOCs

There are 204 current NOCs (National Olympic Committees) within the Olympic Movement. The following tables show the currently used code for each NOC and any different codes used in past Games, per the official reports from those Games. Some of the past code usage is further explained in the following sections. Codes used specifically for a Summer Games only or a Winter Games only, within the same year, are indicated by S and W respectively.

Country code

‘Country codes’ are short alphabetic or numeric geography|geographical codes (geocodes) developed to represent country|countries and dependent areas, for use in data processing and communications. Several different systems have been developed to do this. The best known of these is ISO 3166-1. The term country code frequently refers to international dialing codes, the E.164 list of country calling codes|country calling codes.

Country code – ISO 3166-1

This standard defines for most of the countries and dependent areas in the world:

Country code – ISO 3166-1

*a three-digit numeric (ISO 3166-1 numeric) code.

Country code – ISO 3166-1

The two-letter codes are used as the basis for some other codes or applications, for example,

Country code – ISO 3166-1

*for ISO 4217 currency codes and

Country code – ISO 3166-1

*with deviations, for country code top-level domain names (ccTLDs) on the Internet: list of Internet TLDs.

Country code – Other country codes

*European Union:

Country code – Other country codes

**Before the Enlargement of the European Union|2004 EU enlargement the EU used the UN Road Traffic Conventions List of international license plate codes|license plate codes; since then, it uses ISO 3166-1

Country code – Other country codes

**The Nomenclature des unités territoriales statistiques (Nomenclature of territorial units for statistics, NUTS) of the European Union, mostly focusing on subdivisions of the EU member states

Country code – Other country codes

*FIFA (Fédération Internationale de Football Association) assigns a three-letter code (dubbed FIFA Trigramme) to each of its member and non-member countries: List of FIFA country codes

Country code – Other country codes

*Federal Information Processing Standard (FIPS) FIPS 10-4|10-4 defined two-letter codes used by the U.S. government and in the CIA World Factbook: list of FIPS country codes. On September 2, 2008, FIPS 10-4 was one of ten standards withdrawn by NIST as a Federal Information Processing Standard.Federal Register, September 2, 2008 (Volume 73, Number 170), page 51276

Country code – Other country codes

* GOST 7.67: country codes in Cyrillic from the GOST standards committee

Country code – Other country codes

**Aircraft Registration#List of countries/regions and their registration prefixes and patterns|The national prefixes used in aircraft registration numbers

Country code – Other country codes

**International Civil Aviation Organization#Registered codes|Location prefixes in four-character ICAO airport codes

Country code – Other country codes

*International Olympic Committee (IOC) three-letter codes used in sporting events: list of IOC country codes

Country code – Other country codes

*From the International Telecommunication Union (ITU):

Country code – Other country codes

**the E.164 international telephone dialing codes: list of country calling codes with 1-3 digits,

Country code – Other country codes

**the International mobile subscriber identity|E.212 mobile country codes (MCC), for mobile/wireless phone addresses,

Country code – Other country codes

**the first few characters of call signs of radio stations (maritime, aeronautical, amateur radio, broadcasting, and so on) define the country: the ITU prefix,

Country code – Other country codes

**ITU prefix – amateur and experimental stations – The International Telecommunications Union (ITU) assigns national telecommuncation prefixes for amateur radio|amateur and experimental radio use, so that operators can be identified by their country of origin. These prefixes are legally administered by the national entity to which prefix ranges are assigned.

Country code – Other country codes

**Three-digit codes used to identify countries in maritime mobile radio transmissions, known as maritime identification digits

Country code – Other country codes

*License plates for automobiles:

Country code – Other country codes

**Under the 1949 and 1968 United Nations Road Traffic Conventions (distinguishing signs of vehicles in international traffic): List of international license plate codes.

Country code – Other country codes

**Diplomatic license plates in the United States, assigned by the United States Department of State|U.S. State Department.

Country code – Other country codes

*North Atlantic Treaty Organisation (NATO) used two-letter codes of its own: list of NATO country codes. They were largely borrowed from the FIPS 10-4 codes mentioned below. In 2003 the eighth edition of the Standardisation Agreement (STANAG) adopted the ISO 3166 three-letter codes with one exception (the code for Macedonia). With the ninth edition, NATO is transitioning to four- and six-letter codes based on ISO 3166 with a few exceptions and additions

Country code – Other country codes

*United Nations Development Programme (UNDP) also has its own list of List of UNDP country codes|trigram country codes

Country code – Other country codes

* World Intellectual Property Organization (WIPO): WIPO ST.3 gives two-letter codes to countries and regional intellectual property organizations

Country code – Other country codes

*World Meteorological Organization (WMO) has its own list of country codes, used in reporting meteorological observations

Country code – Other country codes

* UIC (the International Union of Railways): UIC Country Codes

Country code – Other country codes

The developers of ISO 3166 intended that in time it would replace other coding systems in existence.

Country code – Other codings

The following can represent countries:

Country code – Other codings

*The initial digits of International Standard Book Numbers (ISBN) are group identifiers for countries, areas, or language regions.

Country code – Other codings

*The first three digits of GS1 Company Prefixes used to identify products, for example, in barcodes, designate (national) numbering agencies.

List of NATO country codes

The digrams match the List of FIPS country codes|FIPS 10-4 codes with a few exceptions.

List of NATO country codes

The ninth edition’s ratification draft was published on July 6, 2005, with a reply deadline of October 6, 2005. It replaces all two- and four-letter codes with ISO or ISO-like three- and six-letter codes. It is intended as a transitional standard: once all NATO nations have updated their information systems, a tenth edition will be published.

List of NATO country codes

For diplomatic reasons,Wikipedia, Macedonia naming issue|Macedonia Naming Issue, 21 Jun 2013 Macedonia is designated as the Former Yugoslav Republic of Macedonia and receives a (temporary) code explicitly different from the ISO one.

List of NATO country codes

The Republic of Palau is also often indicated (at least in the United States) as PW.

List of NATO country codes – Sources

* NATO STANAG 1059 INT (Ed. 7, 2000) Distinguishing Letters for Geographical Entities for Use in NATO

Country codes: T –

|width=10|*|| Some codes are assigned to Taiwan or Taiwan, Province of China, while some are assigned to the Republic of China. See also Chinese Taipei, political status of Taiwan and China and the United Nations.

Country codes: M – , People’s Republic of China|China

Macau Special Administrative Region of the People’s Republic of China

List of UNDP country codes

This is the ‘list of UNDP (United Nations Development Programme) country codes’.

List of UNDP country codes

In addition to countries, codes identify geographical groupings and political entities such as various liberation fronts (not all of which still exist). The purpose of the codes is in part actuarial.

UIC Country Code

The ‘UIC Country Code’ is a two digit-number identifying member countries of the International Union of Railways (UIC). The UIC has issued numbering systems for rolling stock (UIC wagon numbers) and stations that include the country code. The values are defined in UIC leaflet 920-14.

UIC Country Code

Railroads in North America use a system based on company-specific reporting marks, and a similar system, ISO 6346, is used for intermodal containers.

Country codes: O-Q –

† This NATO country code appears in the 9th edition and is also assigned to Palau

GeoTLD – Internationalized country codes

An internationalized country code is similar to a GeoTLD, with two differences: it is a domain used exclusively for a sovereign state. The other difference is that an internationalized country code is considered a ccTLD and not a GeoTLD. More free geographic ccTLD have been applied for and will be active in 2013.

Internationalized country code top-level domain

An ‘internationalized country code top-level domain’ (‘IDN ccTLD’ or ‘ccIDN’) is a top-level domain (TLD) in the Domain Name System (DNS) of the Internet

Internationalized country code top-level domain

Although the domain class uses the term code, some of these ccTLDs are not codes but full words. For example, ???????? (as-Su??diyya) is not an abbreviation of Saudi Arabia, but the common short-form name of the country in Arabic (language)|Arabic.

Internationalized country code top-level domain

Countries with internationalized ccTLDs also retain their traditional ASCII-based ccTLDs.

Internationalized country code top-level domain – History

The ICANN board approved the establishment of an internationalized top-level domain name working group within the Country Code Names Supporting Organization (ccNSO) in December 2006

Internationalized country code top-level domain – History

# Identify technical basis of the TLD strings and country code specific processes, select IDN ccTLD personnel and authorities, and prepare documentation;

Internationalized country code top-level domain – History

# Perform ICANN due diligence process for technical proposal and publish method;

Internationalized country code top-level domain – History

# Enter delegation process within established IANA procedures.

Internationalized country code top-level domain – History

In May 2010, 21 different countries representing 11 languages, including Chinese, Russian language|Russian, Tamil language|Tamil, and Thai language|Thai, had requested new IDN country codes.

Internationalized country code top-level domain – History

country code, Saudi Arabia ????????., and the United Arab Emirates ??????., (all reading right to left as is customary in Arabic)

Internationalized country code top-level domain – History

, four such TLDs have been implemented: three using the Arabic alphabet, ?????????., ???. and ???????. (for Egypt, Saudi Arabia and the United Arab Emirates, respectively), and one using Cyrillic, .?? (for Russia). Five new IDN ccTLDs using Chinese characters were approved in June 2010:

Internationalized country code top-level domain – History

.?? with variant .?? (for mainland China), .?? (for Hong Kong), and with variant (for Taiwan). (Note: In these minutes, the encodings of the two CNNIC-delegated ccTLDs have inadvertently been swapped.)

Internationalized country code top-level domain – History

According to Egypt’s communication and information technology minister, three Egyptian companies were the first to receive domain licenses on the new masr [??? transliterated] country code

Internationalized country code top-level domain – History

Five new ccTLDs using Chinese characters, the first using a non-alphabetic writing system, were approved by the ICANN Board on June 25, 2010:

Internationalized country code top-level domain – History

* .?? (encoded as .xn--fiqs8s) and .?? (encoded as .xn--fiqz9s; .:wikt:Zh?ngguó|zhongguo), delegated to China Internet Network Information Center (CNNIC), the registrar for ccTLD .cn;

Internationalized country code top-level domain – History

The dual domains delegated to each of CNNIC and TWNIC are synonymous, being purely orthographical variations differing only in using Simplified Chinese characters|simplified forms (w:?|? and w:?|?), as preferred in mainland China, versus Traditional Chinese characters|traditional forms of the same characters (w:?|? and w:?|?), as used in Taiwan.

Internationalized country code top-level domain – History

Ukrainian string .??? was approved by the ICANN Board on February 28, 2013. The zone was added to the root servers on March 19, 2013.

Telephone numbering plan – Country code

By convention, international telephone numbers are indicated by prefixing the country code with a plus sign (+), which is meant to indicate that the subscriber must dial the international dialing prefix in the country from which the call is placed

.sa – Internationalized country code TLD

Saudi Arabia was one of the first countries to apply for the new internationalized domain name (IDN) country code top-level domains authorized by the ICANN|Internet Corporation for Assigned Names and Numbers (ICANN) in 2009. In January 2010, ICANN announced that the Saudi IDN ccTLD (xn--mgberp4a5d4ar, ????????) one of the first four new IDN ccTLDs to have passed the Fast Track String Evaluation within the domain application process.

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Control Code

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Control Code

C0 and C1 control codes Protocols interoperability and usage

Very few applications interpret the other C0 and C1 control codes, as they are not needed for plain text.

C0 and C1 control codes Protocols interoperability and usage

The official English language names of some control codes were revised in the most recent edition of the standard for control codes in general (ISO 6429:1992 or ECMA-48:1991) to be neutral with respect to the graphic characters used with them, and to not assume that, as in the Latin script, lines are written on a page from top to bottom and that characters are written on a line from left to right

C0 and C1 control codes C0 (ASCII and derivatives)

These are the standard ASCII control codes. If using the ISO/IEC 2022 extension mechanism, they are designated as the active C0 control character set with the octet sequence 0x1B 0x21 0x40 (ESC ! @).

C0 and C1 control codes C0 (ASCII and derivatives)

To provide disambiguation between the two potential uses of backspace, the cancel character control code was made part of the standard C1 control set.

C0 and C1 control codes C0 (ASCII and derivatives)

^P 16 10 DLE ? Data Link Escape Cause the following octets to be interpreted as raw data, not as control codes or graphic characters. Returning to normal usage would be implementation dependent.

C0 and C1 control codes C0 (ASCII and derivatives)

^Q 17 11 DC1 ? Device Control One (XON) These four control codes are reserved for device control, with the interpretation dependent upon the device they were connected. DC1 and DC2 were intended primarily to indicate activating a device while DC3 and DC4 were intended primarily to indicate pausing or turning off a device. In actual practice DC1 and DC3 (known also as XON and XOFF respectively in this usage) quickly became the de facto standard for software flow control.

C0 and C1 control codes C0 (ASCII and derivatives)

^[ 27 1B ESC ? Escape \e The Esc key on the keyboard will cause this character to be sent on most systems. It can be used in software user interfaces to exit from a screen, menu, or mode, or in device-control protocols (e.g., printers and terminals) to signal that what follows is a special command sequence rather than normal text. In systems based on ISO/IEC 2022, even if another set of C0 control codes are used, this octet is required to always represent the escape character.

C0 and C1 control codes C1 set

These are the most common extended control codes. If using the ISO/IEC 2022 extension mechanism, they are designated as the active C1 control character set with the sequence 0x1B 0x22 0x43 (ESC ” C). Individual control functions can be accessed with the 7-bit equivalents 0x1B 0x40 through 0x1B 0x5F (ESC @ through ESC _).

C0 and C1 control codes C1 set

] 157 9D OSC Operating System Command Followed by a string of printable characters (0x20 through 0x7E) and format effectors (0x08 through 0x0D), terminated by ST (0x9C). These three control codes were intended for use to allow in-band signaling of protocol information, but are rarely used for that purpose.

Plain text – Control codes

In 8-bit character sets such as Latin-1 and the other ISO 8859 sets, the first 32 characters of the “upper half” (128 to 159) are also control codes, known as the “C1 set” as opposed to the “C0” set just described

C0 and C1 control codes

The ‘C0 and C1 control code’ or control character sets define control codes for use in text by computer systems that use the ISO/IEC 2022 system of specifying control and graphic characters. Most character encodings, in addition to representing printable characters, also have characters such as these that represent non-printable character|additional information about the text, such as the position of a cursor, an instruction to start a new line, or a message that the text has been received.

C0 and C1 control codes – C1 set

These are the most common extended control codes. If using the ISO/IEC 2022 extension mechanism, they are designated as the active C1 control character set with the sequence 0x1B 0x22 0x43 (ESC C). Individual control functions can be accessed with the 7-bit equivalents 0x1B 0x40 through 0x1B 0x5F (ESC @ through ESC _).

Control code – How control characters map to keyboards

In either case, this produces one of the 32 ASCII control codes between 0 and 31

Control code – Printing and display control

With the advent of computer terminals that did not physically print on paper and so offered more flexibility regarding screen placement, erasure, and so forth, printing control codes were adapted

Control code – Transmission control

The device control codes (DC1 to DC4) were originally generic, to be implemented as necessary by each device

Control code – Transmission control

The data link escape character (C0 and C1 control codes|DLE) was intended to be a signal to the other end of a data link that the following character is a control character such as STX or ETX. For example a packet may be structured in the following way (C0 and C1 control codes|DLE) (C0 and C1 control codes|DLE) .

Control code – Miscellaneous codes

For example, code 22, synchronous idle (C0 and C1 control codes|SYN), was originally sent by synchronous modems (which have to send data constantly) when there was no actual data to send

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Co-Creation

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Co-Creation

Social Media Distinction from other media

E-commerce businesses may refer to Social Media as consumer-generated media (CGM). A common thread running through all definitions of Social Media is a blending of technology and social interaction for the co-creation of value.

Social Media Trustworthiness

Since large-scale collaborative co-creation is one of the main way forming information in the social network, Aniket Kittur and Bongowon Suh took under examination and indicated that, “One possibility is that distrust of Content is not due to the inherently mutable nature of the system but instead to the lack of available information for judging trustworthiness.”

Ken Thompson – Fellow of the Computer History Museum

In 1997, both Thompson and Ritchie were inducted as Fellows of the Computer History Museum for “the co-creation of the UNIX operating system, and for development of the C programming language.”

Dennis Ritchie – Awards

In 1997, both Ritchie and Thompson were made Fellows of the Computer History Museum, “for co-creation of the UNIX operating system, and for development of the C programming language.”

Engagement marketing

Rather than looking at consumers as passive receivers of messages, engagement marketers believe that consumers should be actively involved in the production and co-creation of marketing programs, developing a relationship with the brand.

Engagement marketing – The Brand Experience

Going further Engagement Marketing is premised upon: transparency – interactivity – immediacy – facilitation – engagement – co-creation – collaboration – experience and trust, these words define the migration from mass media to Social Media.

Transhumanism – Hubris

Religious thinkers allied with transhumanist goals, such as the theologians Ronald Cole-Turner and Ted Peters, reject the first argument, holding that the doctrine of “co-creation” provides an obligation to use genetic engineering to improve human biology.

Infosys – Initiatives

Infosys’ Global Academic Relations team forges Academic Entente (AcE) with academic and partner institutions. It explores co-creation opportunities between Infosys and academia through case studies, student trips and speaking engagements. They also collaborate on technology, emerging economies, globalization, and research. Some initiatives include research collaborations, publications, conferences and speaking sessions, campus visits and campus hiring.

Nike+iPod – Overview

This product has brought mobile technology, online communities, and athletic communities together and has expanded the field for co-creation.

Jack Kirby – Marvel Comics in the Silver Age (1958–1970)

There have been a number of reasons given for this dissatisfaction, including resentment over Stan Lee’s increasing media prominence, a lack of full creative control, anger over breaches of perceived promises by publisher Martin Goodman, and frustration over Marvel’s failure to credit him specifically for his story plotting and for his character creations and co-creations.Evanier, King of Comics, p

Stan Lee – Early career

Lee’s first superhero co-creation was the Destroyer (Timely Comics)|Destroyer, in Mystic Comics No

Transhumanist – Hubris

Religious thinkers allied with transhumanist goals, such as the theologians Ronald Cole-Turner and Ted Peters (theologian)|Ted Peters, reject the first argument, holding that the doctrine of co-creation provides an obligation to use genetic engineering to improve human biology.

Ward Cunningham

He currently lives in Beaverton, Oregon, and is the Co-Creation Czar for CitizenGlobal. He is Nike, Inc.|Nike’s first Code for a Better World Fellow.

Consumer-to-business

‘Consumer-to-business’ (‘C2B’) is a business model in which consumers (individuals) create value, and firms consume this value. For example, when a consumer writes reviews, or when a consumers gives a useful idea for new product development, then this individual is creating value to the firm, if the firm adopts the input. Excepted concepts are crowd sourcing and co-creation.

Collective intelligence – Examples

In Learner generated context a group of users marshal resources to create an ecology that meets their needs often (but not only) in relation to the co-configuration, co-creation and co-design of a particular learning space that allows learners to create their own context.Luckin, R., du Boulay, B., Smith, H., Underwood, J., Fitzpatrick, G., Holmberg, J., Kerawalla, L., Tunley, H., Brewster, D

Sense Worldwide

In 1999 it started the Sense Network, described as ‘one of the earliest web-based communities’.Richard Donkin The Future of Work 2010 pp125-126 It was one of the first commercial practitioners of co-creation http://blogs.hbr.org/cs/2011/02/co-creation.html and Extreme User Research.http://www.fastcodesign.com/1669452/want-breakthrough-ideas-first-listen-to-the-freaks-and-geeks It has influenced the development of Nike, Inc.|Nike Sportswear http://vimeo.com/5606365 interview with Nike and Habbo Hotel among many others.Marketing Magazine Agency of the Year 2009 P 23 It has received funding from NESTA

Sense Worldwide – Co-creation

Sense Worldwide has used co-creation in its work from its inception.http://blogs.hbr.org/cs/2011/02/co-creation.html Its work in this area has caused it to be listed as one of the NESTA Open 100.http://www.openbusiness.cc/2010/01/06/sense-worldwide/ It has also pioneered the commercial use of extreme user research, building on the Lead User ideas of MIT’s Eric von Hippelhttp://www.fastcodesign.com/1669452/want-breakthrough-ideas-first-listen-to-the-freaks-and-geeks

Sense Worldwide – Co-creation

Sense Worldwide introduced co-creation techniques to Nike and continues to work with Nike’s running, sportswear and football businesses.Nike interview http://vimeo.com/5606365 It authored the Discovery Channel report into the lives of young men in Europe.,“Market Research Agency of the Year” Marketing Magazine (9 Dec 2009) P23 and it contributes to the Economist Intelligence Unit’s reports http://www.businessresearch.eiu.com/service-2020.html and the Harvard Business Reviewhttp://blogs.hbr.org/cs/2011/02/co-creation.html

Metadesign – History

(2003) Principles of Metadesign: processes and levels of co-creation in the new design space

Timeline of historic inventions – 1950s

*1958-59: Co-creation of the integrated circuit by Jack Kilby and Robert Noyce.

Mass customization – Variants

* Collaborative customization – (also considered co-creation) firms talk to individual customers to determine the precise product (business)|product offering that best serves the customer’s needs (see personalized marketing and personal marketing orientation)

Co-creation

‘Co-creation’ is a form of marketing strategy or business strategy that emphasizes the generation and ongoing realization of mutual firm-customer value. It views markets as forums for firms and active customers to share, combine and renew each other’s resources and capabilities to create value through new forms of interaction, service and learning mechanisms. It differs from the traditional active firmpassive consumer market construct of the past.

Co-creation

Value is co-created with customers if and when a customer is able to personalize his or her experience using a firm’s product-service propositionin the lifetime of its useto a level that is best suited to get his or her job(s) or tasks done and which allows the firm to derive greater value from its product-service investment in the form of new knowledge, higher revenues/profitability and/or superior brand value/loyalty.[http://www.customerthink.com/blog/my_personal_definition_of_business_with_customer_value_co_creation Wim Rampen – My Personal Definition of Business with Customer Value Co-Creation and comments by Chris Lawer].

Co-creation

Co-creation in their view not only describes a trend of jointly creating products

Co-creation – From co-production to co-creation

Michel, Vargo and Lusch recognize the influence of Normann on their own work and acknowledge similarity between the concepts of co-production and co-creation: his customer co-production mirrors the similar concept found in FP6.Michel, S.; Vargo, S

Co-creation – From co-production to co-creation

In his letter, he uses the word co-creation and states at the core of collaboration is co-creation: customers aren’t just customizing; they’re collaborating with vendors to create unique value.

Co-creation – From co-production to co-creation

Once used sporadically by other authors (for instance Schrage in 1995), we can therefore say that the official debut of value co-creation takes place 2004

Co-creation – From co-production to co-creation

The authors see the co-creation of value as an initiative of the customers who are dissatisfied with available choices [and] want to co-create value and thereby co-create value. The co-creation of value is conceptualized thanks to a model called DART (for dialogue, access, risk-benefits, transparency).

Co-creation – From co-production to co-creation

From 2004 onwards, publications on value co-creation tend to flourish because of the resonance of Vargo’s and Lush’s ideas

Co-creation – From co-production to co-creation

It says basically that the firm should monitor the customer co-creation and therefore set KPI’s on it.

Co-creation – From co-production to co-creation

The important underlying question is that the debate around co-creation has somewhat blurred the entity at the origin of value

Co-creation – From co-production to co-creation

Second are supplier value-creation processes based on co-creation opportunities (through technological breakthrough, changes in industry logics, changes in customers preferences and lifestyles), planning, implementation and metrics

Co-creation – Early applications of co-creation

The introduction of enterprise social software may have functioned as an enabler of this change in how companies evolve to business networks, and how both large and small companies cooperate. But Prahalad and Ramaswamy stated in their published work, as other practitioners have affirmed, that co-creation is about far more than customers co-designing products and services.

Co-creation – Early applications of co-creation

Co-creation is at the heart of the open-source software|open-source-software movement, where users have full access to the source code and are empowered to make their own changes and improvements to it.

Co-creation – Early applications of co-creation

Co-creation can be thought to have its roots in the work of Herstatt and Von Hippel at Hilti, where they worked with lead users on innovative products.

Co-creation – Early applications of co-creation

They are no longer in the business of product and service design, he stated; they are really in the business of customer co-creation.[http://www.businessweek.com/innovate/NussbaumOnDesign/archives/2006/01/ces–when_consu.html Nussbaum on Design]

Co-creation – Early applications of co-creation

During the mid-2000s, co-creation became a driving concept in Social Media and marketing techniques, where companies such as Converse (shoe company)|Converse persuaded large numbers of its most passionate customers to create their own video advertisements for the product. The Web 2.0 phenomenon encompassed many forms of co-creation marketing, as social and consumer communities became ambassadors, buzz agents, smart mobs, and participants transforming the product experience.

Co-creation – Early applications of co-creation

Other examples of co-creation can be found in arts. Chaney, D. (2012). The Music Industry in the Digital Age: Consumer Participation in Value Creation. International Journal of Arts Management, 15(1), 42-52.

Co-creation – Co-creation and corporate management

Customer-facing functions such as sales or customer service were also opened up to co-creation at companies including Starbucks and Dell Computer

Co-creation – Co-creation and corporate management

Authors published bestselling books developing theories influenced by co-creation and customer collaboration

Co-creation – Co-creation and corporate management

Co-creation became global, as practices reached senior managers at companies in Europe and Asia including Linux (open software), Procter Gamble’s Connect Develop (dramatically improved research productivity through reliance on global collaboration platform with people outside PG), and InnoCentive (a research collective in the pharmaceutical industry).

Co-creation – Co-creation and corporate management

Of this rapid morphing of co-creation, Ramaswamy and his co-author Francis Gouillart wrote: Through their interactions with thousands of managers globally who had begun

Co-creation – Co-creation and corporate management

experimenting with co-creation, they discovered that enterprises were building platforms that engaged not only the firm and its customers but also the entire network of suppliers, partners, and employees, in a continuous development of new experiences with individuals.Ramaswamy, Venkat; Gouillart, Francis (2010). The Power of Co-Creation: Build It with Them To Boost Growth, Productivity, and Profits. Free Press (publisher)|Free Press.

Co-creation – Third stage of co-creation

Co-creation is seismic shift in thinking from the industrial age mind set to people engagement mindset.’

Co-creation – Third stage of co-creation

Leveious Rolando asked a question in Wired Magazine Blog that Co-creation that Early 2013, consumers have become primary drivers of content, of product and of brand. Able to upload user-generated video to YouTube and broadcast affinities to Twitter fans, consumers have overturned the traditional model where a business builds a product in silo and offers it up to consumers. Sound archaic? Leveious says the Danish coined the new word Co-creation and methodology The Need for Co-creating UX

Co-creation – Third stage of co-creation

The UX community today is subjected to close discussions with the Technology and Business teams also. It is these other teams who are also playing a crucial role in the Co-creation process.

Co-creation – Third stage of co-creation

Rolando made the point at a co-creation European conference that www.trendwatching.com words user manufacturing coined by Trendwatching perfectly describes symbiotic relationship developing between people and companies as result customers are becoming not only co-designers, but also manufacturers, using only the infrastructure provided by specialized companies.

Dennis Ritchie – Awards

In 1997, both Ritchie and Thompson were made Fellows of the Computer History Museum, for co-creation of the UNIX Operating System, and for development of the C programming language.

Open design

Open design is a form of co-creation, where the final product is designed by the users, rather than an external stakeholder such as a private company.

Ken Thompson (computer programmer) – Fellow of the Computer History Museum

In 1997, both Thompson and Ritchie were inducted as Fellows of the Computer History Museum for the co-creation of the UNIX operating system, and for development of the C programming language.

Transformation design – Process

With so many points-of-view brought into the process, transformation designers are not always ‘designers.’ Instead, they often play the role of moderator. Though varying methods of participation and co-creation, these moderating designers create hands-on, collaborative workshops (a.k.a. charrette) that make the design process accessible to the non-designers.

Co-design

Co-design is often used by trained designers who recognize the difficulty in properly understanding the cultural, societal, or usage scenarios encountered by their user. C. K. Prahalad and Venkat Ramaswamy are usually given credit for bringing co-creation/co-design to the minds of those in the business community with the 2004 publication of their book, The Future of Competition: Co-Creating Unique Value with Customers. They propose:

Online research community

Online technology can adapt to almost any research need, be it showing creative stimulus material, gathering ideas for innovation and co-creation or simply an instant ‘go/no go’ when you need it

Woody Harrelson – Environmental

PICNIC describes its annual festival as three intensive days [when] we mix creativity, science, technology, media and business to explore new solutions in the spirit of co-creation.[http://www.picnicnetwork.org/festival] He once scaled the Golden Gate Bridge in San Francisco with members of North Coast Earth First! group to unfurl a banner that read, Hurwitz, Aren’t ancient redwoods more precious than gold? in protest of Maxxam Inc/PALCO CEO Charles Hurwitz, who once stated, He who has the gold, makes the rules.

ModulArt – Co-creativity in Modular Art

Co-creation is closely associated with mass customization, a production model that combines the opportunity for individual personalization with mass production

Gestalt therapy – Self

In Gestalt therapy, the process is not about the self of the client being helped or healed by the fixed self of the therapist, rather it is an exploration of the co-creation of self and other in the here-and-now of the therapy

Vision Critical – Business

* IdeaHub: a co-creation tool that tap into the ideas of customers, employees or other stakeholders for innovation and new product development.

Ethnocinema – Toward a contemporary ethnocinema: some contradictions

Obviously, to achieve a wide viewing audience, formal concerns cannot be completely ignored, but these aesthetic concerns are addressed together in the co-creation of the films

Co-marketing – Co-creative marketing

The co-creation of a company and consumers are contained in the co-marketing.

Schema Therapy – Flashcards

They are developed by the therapist or a co-creation of therapist and patient and are statements that would be similar to those made by a parent to a young child at the developmental age that the patient currently experiences their Vulnerable Child mode

Angela (comics) – Legal rights

McFarlane had initially agreed that Gaiman retained creator rights on the characters, but later claimed that Gaiman’s work had been work-for-hire and that McFarlane owned all of Gaiman’s co-creations entirely, pointing to the legal indicia in Spawn #9 and the lack of legal contract stating otherwise

Roger Stern – Comics

Other work for DC included a relaunched Ray Palmer (comics)|Atom series drawn by Dwayne Turner and the co-creation of the Will Payton version of Starman (comics)|Starman with artist Tom Lyle.Manning 1980s in Dolan, p

Michael Golden (comics)

‘Michael Golden’ is an United States|American comic book artist and writer best known for his late-1970s work on Marvel Comics’ Micronauts (comics)|The Micronauts, as well as his co-creation of the characters Rogue (comics)|Rogue and Bucky O’Hare.

Ferret (comics) – Father Time

One of future Marvel patriarch Stan Lee’s first co-creations, Father Time starred in a backup feature in Captain America Comics #6-12 (Sept. 1941 – March 1942), by which time it was being drawn by Jack Alderman. The feature also appeared in Young Allies Comics #3 (Spring 1942), and Mystic Comics #10 (Aug. 1942).

Bryan Elsley

‘Bryan Elsley’ (born 17 May 1961 in Dalkeith, Midlothian) is a Scotland|Scottish television writer, best known for the co-creation of E4 (channel)|E4 teen television series|drama Skins (UK TV series)|Skins with his son, Jamie Brittain. Other television dramas include 40 (TV serial)|40, Rose and Maloney, Nature Boy (TV serial)|Nature Boy, The Young Person’s Guide to Becoming a Rock Star, The Crow Road (TV series)|The Crow Road, Dates (TV series)|Dates, and Govan Ghost Story.

IN2015

‘Intelligent Nation 2015’ (iN2015) is a 10-year masterplan by the Government of Singapore to help Singapore realise the potential of infocomm over the next decade. Led by the Infocomm Development Authority of Singapore (IDA), iN2015 is a multi-agency effort that is the result of private, public and people sector co-creation.

Greg Rucka – Career

While writing Detective Comics, he created a number of background characters that led to the co-creation of Gotham Central with co-writer Ed Brubaker

Service-dominant logic – What is S-D Logic?

Over the past several decades, new perspectives have emerged that have a revised logic focused on intangible resources, the co-creation of value, and relationships

Service-dominant logic – Axioms of S-D logicVargo, Stephen L. Service-dominant logic reframes (service) innovation. Highlights in service research, VTT, 2013

Network Intersections,Value-in-Context, and the Co-Creation of Markets. Marketing Theory, 2011. the collaborative nature of value creation becomes even more apparent. That is, value co-creation through service-for-service exchange is at the very heart of society.Lusch, R.F. Vargo, S. L. Service dominant logic: reactions, reflections and refinements.” Marketing Theory, Vol. 6, No. 3, 2006, pp. 281–288.

Service-dominant logic – Axioms of S-D logicVargo, Stephen L. Service-dominant logic reframes (service) innovation. Highlights in service research, VTT, 2013

This is different from co-creation of value, which is intended to capture the essential nature of value creation: it always involves the beneficiary’s participation (through use, integration with other resources, etc,) in some manner.

Service-dominant logic – Axioms of S-D logicVargo, Stephen L. Service-dominant logic reframes (service) innovation. Highlights in service research, VTT, 2013

It sets the stage for thinking about the mechanics and the networked nature of value co-creation, as well as the process through which the resources for service provision are created, the integration of resources, resources from various market-facing, public, and private sources

Service-dominant logic – The Service Ecosystem

A service-centered approach to social and economic exchange broadens the process of value creation beyond a firm’s operation activities to include the active participation of customers and other stakeholders, through co-creation (FP6)

Service-dominant logic – Conclusion

A goods dominant marketing logic arguably limits the mind-set for seeing the opportunities for co-creation of value with customers and other stakeholders of the firm

Friday Night Videos – Early years

Ebersol departed from The Midnight Special in 1981 to take over as the executive producer at his co-creation with Lorne Michaels, Saturday Night Live

Improvisational comedy – Structure and process

In order for an improvised scene (fiction)|scene to be successful, the improvisers involved must work together responsively to define the parameters and action of the scene, in a process of co-creation

Len Wein

‘Len Wein’ (; born June 12, 1948) is an American comic book writer and editor best known for co-creating DC Comics’ Swamp Thing and Marvel Comics’ Wolverine (character)|Wolverine, and for helping revive the Marvel superhero team the X-Men (including the co-creation of Nightcrawler (comics)|Nightcrawler, Storm (Marvel Comics)|Storm, and Colossus (comics)|Colossus). Additionally, he was the editor for writer Alan Moore and illustrator Dave Gibbons’ influential DC miniseries Watchmen.

Raymond Weil – Engagement in the Arts Music

‘New Music Talents :’ Raymond Weil Genève organised in 2011 a New Music Talent competition inviting amateur musicians to create a track inspired by the Swiss watch brand. The contest was hosted on eYeka’s co-creation platform and the winner was awarded a 5.000USD cash prize, as well as a Raymond Weil Genève timepiece and the promotion of the winning track on the brand’s website and Facebook page.

Steven Levitan – Career

It is under this company that they produced their co-creations Back to You (TV series)|Back to You and Modern Family

Female comics creators – France/Belgium

One of the earliest successful female artists was Claire Bretécher, who started her career in the 60’s and is famed for her humor series Les Frustrés and the co-creation of the magazine L’Écho des savanes along with Gotlib and Mandryka.

Dave Cockrum

was an American comic book artist known for his co-creation of the new X-Men characters Nightcrawler (comics)|Nightcrawler, Storm (Marvel Comics)|Storm, and Colossus (comics)|Colossus

Gerry Conway – Early career

He scripted the first Man-Thing story, in 1971, sharing co-creation credit with Stan Lee and Roy Thomas

Gerry Conway – DC Comics and later career

Two other Conway co-creations, the Deserter (with artist Dick Ayers) and the Vixen (comics)|Vixen (with artist Bob Oksner)Wells p

Marc Warren (TV producer) – Career

It was there he met Dennis Rinsler before moving to Los Angeles.[http://www.abcmedianet.com/web/showpage/showpage.aspx?program_id=001439type=producers Dennis Rinsler Marc Warren Executive Producers of That’s So Raven] – ABC Medianet Their experiences as teachers was the inspiration for the 1990s sitcom Nick Freno: Licensed Teacher starring Mitch Mullany, which they also produced with Warren receiving co-creation credit with Richard Gurman

Ace Kilroy

The co-creation of artists Rob Kelly and Dan O’Connor (who met while attending the Joe Kubert School of Graphic Art, Ace Kilroy features the titular character–a WWI Veteran turned soldier of fortune who has experience in the strange and the unusual

Konga (film)

The film was the basis for a comic-book series published by Charlton Comics and initially drawn by Steve Ditko (prior to Ditko’s co-creation of Spider-Man) in the 1960s.

Doug Wildey

Retrieved on March 6, 2013 (May 2, 1922 – October 5, 1994) was a cartoonist and comic book artist best known for his co-creation of the 1964 animated television series, Jonny Quest (TV series)|Jonny Quest for Hanna-Barbera Productions.

The Bojeffries Saga – Publication history

In 1992, Tundra Press (the company set up by Kevin Eastman with profits from his co-creation of the Teenage Mutant Ninja Turtles) reprinted the ten Bojeffries stories together with an introduction from Lenny Henry and four new illustration-stories: three cut-outs and a recipe.

Experiential marketing

Rather than looking at consumers as passive receivers of messages, engagement marketers believe that consumers should be actively involved in the production and co-creation of marketing programs, developing a relationship with the brand.

Greenstone (software)

Co-creation and development of digital library software

Brian Miner

‘Brian Daniel Miner’ (born March 27, 1981) is an American comedian and satire|satirist. He is known for his co-creation of the live sketch comedy series The Crippling Thoughts of Victor Bonesteel along with fellow writer and comedian Bryan Finnigan [http://media.www.theorion.com/media/storage/paper889/news/2005/05/11/Entertainment/Sketch.Comedy.Spoofs.Life-1508061.shtml] [http://www.newsreview.com/chico/Content?oid=34848].

Delo – History and profile

For more than 50 years it has been involved in active co-creation of the Slovenian public space. It covers politics, economics, sports, culture and social events in Slovene language. In addition to Slovenia, the paper is available in several Croatian cities and in Belgrade, Serbia.

Digital Extremes

‘Digital Extremes’ is a Canada|Canadian computer and video game developer founded in 1993 by James Schmalz, best known for its co-creation of Epic Games’ highly successful Unreal (series)|Unreal series of games. Digital Extremes is headquartered in London, Ontario.

Correllian Nativist Tradition – Milestones

* Under Lady Krystel and Chancellor Don’s leadership (since 1979), the tradition sought to expand its outreach and began an ongoing educational program which resulted in the co-creation of the Internet-based Witch School with Ed Hubbard of Psychic Services Incorporated in 2001. The Witch School has since become an independent, multi-tradition entity.

For More Information, Visit:

https://store.theartofservice.com/the-co-creation-toolkit.html

https://store.theartofservice.com/the-co-creation-toolkit.html

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Check Digit

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Check Digit

International Standard Book Number Check digits

A check digit is a form of redundancy check used for error detection, the decimal equivalent of a binary check bit. It consists of a single digit computed from the other digits in the message.

International Standard Book Number ISBN-10 check digits

The 2001 edition of the official manual of the International ISBN Agency says that the ISBN-10 check digit – which is the last digit of the ten-digit ISBN – must range from 0 to 10 (the symbol X is used for 10), and must be such that the sum of all the ten digits, each multiplied by its (integer) weight, descending from 10 to 1, is a multiple of 11.

International Standard Book Number ISBN-10 check digits

Formally, using modular arithmetic, we can say:

International Standard Book Number ISBN-10 check digits

It is also true for ISBN-10’s that the sum of all the ten digits, each multiplied by its weight in ascending order from 1 to 10, is a multiple of 11. For this example:

International Standard Book Number ISBN-10 check digits

The two most common errors in handling an ISBN (e.g., typing or writing it) are a single altered digit or the transposition of adjacent digits. It can be proved that all possible valid ISBN-10’s have at least two digits different from each other. It can also be proved that there are no pairs of valid ISBN-10’s with eight identical digits and two transposed digits. (These are true only because the ISBN is less than 11 digits long, and because 11 is prime.)

International Standard Book Number ISBN-10 check digits

The ISBN check digit method therefore ensures that it will always be possible to detect these two most common types of error, i.e. if either of these types of error has occurred, the result will never be a valid ISBN – the sum of the digits multiplied by their weights will never be a multiple of 11. However, if the error occurs in the publishing house and goes undetected, the book will be issued with an invalid ISBN.

International Standard Book Number ISBN-10 check digits

In contrast, it is possible for other types of error, such as two altered non-transposed digits, or three altered digits, to result in a valid ISBN number (although it is still unlikely).

International Standard Book Number ISBN-10 check digit calculation

Modular arithmetic is convenient for calculating the check digit using modulus 11. Each of the first nine digits of the ten-digit ISBN – excluding the check digit, itself – is multiplied by a number in a sequence from 10 to 2, and the remainder of the sum, with respect to 11, is computed. The resulting remainder, plus the check digit, must equal 11; therefore, the check digit is 11 minus the remainder of the sum of the products.

International Standard Book Number ISBN-10 check digit calculation

For example, the check digit for an ISBN-10 of 0-306-40615-? is calculated as follows:

International Standard Book Number ISBN-10 check digit calculation

Thus the check digit is 2, and the complete sequence is ISBN 0-306-40615-2. The value required to satisfy this condition might be 10; if so, an ‘X’ should be used.

International Standard Book Number ISBN-13 check digit calculation

The 2005 edition of the International ISBN Agency’s official manual describes how the 13-digit ISBN check digit is calculated.

International Standard Book Number ISBN-13 check digit calculation

The calculation of an ISBN-13 check digit begins with the first 12 digits of the thirteen-digit ISBN (thus excluding the check digit itself). Each digit, from left to right, is alternately multiplied by 1 or 3, then those products are summed modulo 10 to give a value ranging from 0 to 9. Subtracted from 10, that leaves a result from 1 to 10. A zero (0) replaces a ten (10), so, in all cases, a single check digit results.

International Standard Book Number ISBN-13 check digit calculation

For example, the ISBN-13 check digit of 978-0-306-40615-? is calculated as follows:

International Standard Book Number ISBN-13 check digit calculation

Thus, the check digit is 7, and the complete sequence is ISBN 978-0-306-40615-7.

International Standard Book Number ISBN-13 check digit calculation

Formally, the ISBN-13 check digit calculation is:

International Standard Book Number ISBN-13 check digit calculation

The ISBN-10 formula uses the prime modulus 11 which avoids this blind spot, but requires more than the digits 0-9 to express the check digit.

International Standard Book Number ISBN-13 check digit calculation

Additionally, if the sum of the 2nd, 4th, 6th, 8th, 10th, and 12th digits is tripled then added to the remaining digits (1st, 3rd, 5th, 7th, 9th, 11th, and 13th), the total will always be divisible by 10 (i.e., end in 0).

International Standard Book Number ISBN-13 check digit calculation

public static boolean isISBN13Valid(String isbn) {

International Standard Book Number ISBN-13 check digit calculation

check += Integer.valueOf(isbn.substring(i, i + 1));

International Standard Book Number ISBN-13 check digit calculation

function isValidISBN13(ISBNumber) {

International Standard Book Number ISBN-13 check digit calculation

sum = transformed_digits.reduce(:+)

International Standard Book Number ISBN-13 check digit calculation

check = (10 – (sum(int(digit) * (3 if idx % 2 else 1) for idx, digit in enumerate(isbn[:12])) % 10)) % 10

International Standard Book Number ISBN-13 check digit calculation

FUNCTION validate_isbn_13(isbn VARCHAR2) RETURN INTEGER IS

International Standard Book Number ISBN-13 check digit calculation

modular INTEGER;

International Standard Book Number ISBN-13 check digit calculation

IF modular = 0 THEN

International Standard Book Number ISBN-13 check digit calculation

reminder := 10 – modular;

International Standard Book Number ISBN-13 check digit calculation

IF TO_CHAR( reminder ) = SUBSTR(isbn, 13, 1 ) THEN

International Standard Book Number ISBN-13 check digit calculation

# Bourne-Again Shell

International Standard Music Number – Check digit

To calculate the check digit, each digit of the ISMN is multiplied by a weight, alternating 1 and 3 left to right. These weighted digits are added together. The check digit is the integer between 0 and 9 that makes the sum a multiple of 10.

ISO/IEC 7812 – Check digit

The final digit is a check digit which is calculated using the Luhn algorithm, defined in Annex B of ISO/IEC 7812-1.

ISO 6346 – Check Digit

The check digit consists of one numeric digit providing a means of validating the recording and transmission accuracies of the owner code and serial number.

International Bank Account Number – Generating IBAN check digits

According to the ECBS “generation of the IBAN shall be the exclusive responsibility of the bank/branch servicing the account”. The ECBS document replicates part of the ISO/IEC 7064:2003 standard as a method for generating check digits in the range 02 to 98. Check digits in the ranges 00 to 96, 01 to 97, and 03 to 99 will also provide validation of an IBAN, but the standard is silent as to whether or not these ranges may be used.

International Bank Account Number – Generating IBAN check digits

Check that the total IBAN length is correct as per the country. If not, the IBAN is invalid

International Bank Account Number – Generating IBAN check digits

Replace the two check digits by 00 (e.g. GB00 for the UK)

International Bank Account Number – Generating IBAN check digits

Move the four initial characters to the end of the string

International Bank Account Number – Generating IBAN check digits

Replace the letters in the string with digits, expanding the string as necessary, such that A or a = 10, B or b = 11, and Z or z = 35. Each alphabetic character is therefore replaced by 2 digits

International Bank Account Number – Generating IBAN check digits

Calculate mod-97 of the new number, which results in the remainder

International Bank Account Number – Generating IBAN check digits

Subtract the remainder from 98, and use the result for the two check digits. If the result is a single digit number, pad it with a leading 0 to make a two-digit number

Codabar – Check digit

Because Codabar is self-checking, most standards do not define a check digit.http://mdn.morovia.com/manuals/bax3/shared.bartech.php#Symbology.Codabar

Codabar – Check digit

Some standards that use Codabar will define a check digit, but the algorithm is not universal. For purely numerical data, such as the library barcode pictured above, the Luhn algorithm is popular.http://www.makebarcode.com/specs/codabar.html

Codabar – Check digit

When all 16 symbols are possible, a simple modulo-16 checksum is used.http://www.barcodesymbols.com/codabar.htm The values 10 through 19 are assigned to the symbols –$:/+.ABCD, respectively.

Universal Product Code – Check digits

In the UPC-A system, the check digit is calculated as follows:

Universal Product Code – Check digits

# Add the digits in the Even and odd numbers|odd-numbered positions (first, third, fifth, etc.) together and multiply by three.

Universal Product Code – Check digits

# Add the digits in the even and odd numbers|even-numbered positions (second, fourth, sixth, etc.) to the result.

Universal Product Code – Check digits

# Find the result modulo operation|modulo 10 (i.e. the remainder when divided by 10.. 10 goes into 58 5 times with 8 leftover).

Universal Product Code – Check digits

# If the result is not zero, subtract the result from ten.

Universal Product Code – Check digits

For example, in a UPC-A barcode 03600029145’x’ where ‘x’ is the unknown check digit, ‘x’ can be calculated by

Universal Product Code – Check digits

*adding the odd-numbered digits (0+6+0+2+1+5 = 14),

Universal Product Code – Check digits

*calculating modulo ten (58mod10 = 8),

Universal Product Code – Check digits

*subtracting from ten (10minus;8 = 2).

Universal Product Code – Check digits

This should not be confused with the numeral X which stands for a value of 10 in modulo 11, commonly seen in the International Standard Book Number|ISBN check digit.

Code 128 – Check digit calculation

The remainder of the division is the check digit’s ‘value’ which is then converted into a character (following the instructions given Code 128#Conversion to char|below) and appended to the end of the barcode.

Code 128 – Check digit calculation

For example, in the following table, the code 128 variant A checksum value is calculated for the alphanumeric string PJJ123C

Code 128 – Calculating check digit with multiple variants

As Code 128 allows multiple variants, as well as switching between variants within a single barcode, it is important to remember that the absolute Code 128 value of a character is completely independent of its value within a given variant. For instance the Variant C value 33 and the Variant B value A are both considered to be a Code 128 value of 33, and the check digit would be computed based on the value of 33 times the character’s position within the barcode.

British Cattle Movement Service – Check Digit

The check digit for a cow’s ear tag is calculated by dividing the number obtained from the herd mark and animal number by 7 and adding one to the remainder. Take UK herd number 303565 cow number 01234. We work out the check digit as follows:

MSI Barcode – Check digit calculation

The MSI barcode uses one of five possible schemes for calculating a check digit:

MSI Barcode – Check digit calculation

* Modulo operation|Mod 10 (most common)

MSI Barcode – Mod 10 Check Digit

When using the Mod 10 check digit algorithm, a string to be encoded 1234567 will be printed with a check digit of 4:

MSI Barcode – Mod 10 Check Digit

[http://publib.boulder.ibm.com/infocenter/printer/v1r1/index.jsp?topic=/com.ibm.printers.afpproducts/com.ibm.printers.ppfaug/ib6p8mst334.htm IBM Printing Systems Information Center – Check Digit Calculation Method], IBM.

MSI Barcode – Mod 10 Check Digit

uses the Luhn algorithm.

MSI Barcode – Mod 11 Check Digit

2

MSI Barcode – Mod 11 Check Digit

This example is using the IBM modulo 11 algorithm with a weighting pattern of (2,3,4,5,6,7)

MSI Barcode – Mod 11 Check Digit

Let X = the final product of the string to encode.

MSI Barcode – Mod 11 Check Digit

3. Mod the sum by 11, subtract the result from 11, and then apply the mod 11 function again.

MSI Barcode – Mod 1010 check digit

Simply calculate the Mod 10 check digit the first time and then calculate it again with the previous result and append the result of the second Mod 10 Calculation to the string to be encoded.

MSI Barcode – Mod 1110 check digit

Same as Mod 1010 but the first calculation should be a Mod 11 Check digit.

International Mobile Equipment Identity – Check digit computation

The last number of the IMEI is a check digit calculated using the Luhn algorithm.

International Mobile Equipment Identity – Check digit computation

According to the [http://www.gsma.com/documents/ts-06-6-0-imei-allocation-and-approval-guidelines/20164/ IMEI Allocation and Approval Guidelines],

International Mobile Equipment Identity – Check digit computation

The Check Digit shall be calculated according to Luhn algorithm|Luhn formula (ISO/IEC 7812). (See GSM 02.16 / 3GPP 22.016). The Check Digit is a function of all other digits in the IMEI. The Software Version Number (SVN) of a mobile is not included in the calculation.

International Mobile Equipment Identity – Check digit computation

The purpose of the Check Digit is to help guard against the possibility of incorrect entries to the CEIR and EIR equipment.

International Mobile Equipment Identity – Check digit computation

The presentation of the Check Digit both electronically and in printed form on the label and packaging is very important. Logistics (using bar-code reader) and EIR/CEIR administration cannot use the Check Digit unless it is printed outside of the packaging, and on the ME IMEI/Type Accreditation label.

International Mobile Equipment Identity – Check digit computation

The check digit is not transmitted over the radio interface, nor is it stored in the EIR database at any point. Therefore, all references to the last three or six digits of an IMEI refer to the actual IMEI number, to which the check digit does not belong.

International Mobile Equipment Identity – Check digit computation

# Starting from the right, double every other digit (e.g., 7 ? 14).

International Mobile Equipment Identity – Check digit computation

Conversely, one can calculate the IMEI by choosing the check digit that would give a sum divisible by 10. For the example IMEI 49015420323751?,

International Mobile Equipment Identity – Check digit computation

To make the sum divisible by 10, we set ? = 8, so the IMEI is 490154203237518.

Check digit

A ‘check digit’ is a form of redundancy check used for Error detection and correction|error detection on identification numbers (e.g. bank account numbers) which have been input manually. It is analogous to a binary parity bit used to check for errors in computer-generated data. It consists of a single digit (sometimes more than one) computed by an algorithm from the other digits (or letters) in the sequence input.

Check digit

With a check digit, one can detect simple errors in the input of a series of characters (usually digits) such as a single mistyped digit or some permutations of two successive digits.

Check digit – Design

Check digit algorithms are generally designed to capture human transcription errors. In order of complexity, these include the following:

Check digit – Design

* single digit errors, such as 1 ? 2

Check digit – Design

* transposition errors, such as 12 ? 21

Check digit – Design

* twin errors, such as 11 ? 22

Check digit – Design

* jump transpositions errors, such as 132 ? 231

Check digit – Design

* jump twin errors, such as 131 ? 232

Check digit – Design

* phonetic errors, such as 60 ? 16 (sixty to sixteen)

Check digit – Design

In choosing a system, a high probability of catching errors is traded off against implementation difficulty; simple check digit systems are easily understood and implemented by humans but do not catch as many errors as complex ones, which require sophisticated programs to implement.

Check digit – Design

A desirable feature is that left-padding with zeros should not change the check digit. This allows variable length digits to be used and the length to be changed.

Check digit – Design

If there is a single check digit added to the original number, the system will not always capture multiple errors, such as two replacement errors (12 ? 34) though, typically, double errors will be caught 90% of the time (both changes would need to change the output by offsetting amounts).

Check digit – Design

A very simple check digit method would be to take the sum of all digits (digital root|digital sum) modulo operation|modulo 10. This would catch any single-digit error, as such an error would always change the sum, but does not catch any transposition errors (switching two digits) as re-ordering does not change the sum.

Check digit – Design

A slightly more complex method is to take the weighted sum of the digits, modulo 10, with different weights for each number position.

Check digit – Design

To illustrate this, for example if the weights for a four digit number were 5, 3, 2, 7 and the number to be coded was 4871, then one would take 5×4 + 3×8 + 2×7 + 7×1 = 65, i.e. 5 modulo 10, and the check digit would be 5, giving 48715.

Check digit – Design

Using different weights on neighboring numbers means that most transpositions change the check digit; however, because all weights differ by an even number, this does not catch transpositions of two digits that differ by 5, (0 and 5, 1 and 6, 2 and 7, 3 and 8, 4 and 9), since the 2 and 5 multiply to yield 10.

Check digit – Design

The code instead uses modulo 11, which is prime, and all the number positions have different weights 1,2,\dots,10. This system thus detects all single digit substitution and transposition errors (including jump transpositions), but at the cost of the check digit possibly being 10, represented by X. (An alternative is simply to avoid using the serial numbers which result in an X check digit.) instead uses the GS1 algorithm used in EAN numbers.

Check digit – Design

To reduce this failure rate, it is necessary to use more than one check digit (for example, the modulo 97 check referred to below, which uses two check digits – for the algorithm, see International Bank Account Number) and/or to use a wider range of characters in the check digit, for example letters plus numbers.

Check digit – UPC

The final digit of a Universal Product Code is a check digit computed as follows:

Check digit – UPC

# Add the digits (up to but not including the check digit) in the odd-numbered positions (first, third, fifth, etc.) together and multiply by three.

Check digit – UPC

# Add the digits (up to but not including the check digit) in the even-numbered positions (second, fourth, sixth, etc.) to the result.

Check digit – UPC

# Take the remainder of the result divided by 10 (modulo operation) and subtract this from 10 to derive the check digit.

Check digit – UPC

For instance, the UPC-A barcode for a box of tissues is 036000241457. The last digit is the check digit 7, and if the other numbers are correct then the check digit calculation must produce 7.

Check digit – UPC

# Add the odd number digits: 0+6+0+2+1+5 = 14

Check digit – UPC

# To calculate the check digit, take the remainder of (53 / 10), which is also known as (53 modulo 10), and subtract from 10. Therefore, the check digit value is 7.

Check digit – UPC

Another example: to calculate the check digit for the following food item 01010101010.

Check digit – UPC

# To calculate the check digit, take the remainder of (5 / 10), which is also known as (5 modulo 10), and subtract from 10 i.e. (10 – 5 modulo 10) = 5. Therefore, the check digit value is 5.

Check digit – UPC

# If the remainder is 0, subtracting from 10 would give 10. In that case, use 0 as the check digit.

Check digit – ISBN 10

The digit the farthest to the right (which is multiplied by 1) is the check digit, chosen to make the sum correct

Check digit – ISBN 10

While this may seem more complicated than the first scheme, it can be validated simply by adding all the products together then dividing by 11. The sum can be computed without any multiplications by initializing two variables, t and sum, to 0 and repeatedly performing t = t + digit; sum = sum + t; (which can be expressed in C (programming language)|C as sum += t += digit;). If the final sum is a multiple of 11, the ISBN is valid.

Check digit – ISBN 13

ISBN 13 (in use January 2007) is equal to the EAN-13 code found underneath a book’s barcode. Its check digit is generated the same way as the UPC except that the even digits are multiplied by 3 instead of the odd digits.

Check digit – EAN (GLN,GTIN, EAN numbers administered by GS1)

EAN (European Article Number) check digits (administered by GS1) are calculated by summing the odd position numbers and multiplying by 3 and then by adding the sum of the even position numbers. Numbers are examined going from right to left, so the first odd position is the last digit in the code. The final digit of the result is subtracted from 10 to calculate the check digit (or left as-is if already zero).

Check digit – EAN (GLN,GTIN, EAN numbers administered by GS1)

A GS1 check digit calculator and detailed documentation is online at GS1’s website.

Check digit – EAN (GLN,GTIN, EAN numbers administered by GS1)

Another official calculator page shows that the mechanism for GTIN-13 is the same for Global Location Number/GLN.

Check digit – International

* The International SEDOL number.

Check digit – International

* The final digit of an ISSN code or IMO Number.

Check digit – International

* The International Securities Identifying Number (ISIN).

Check digit – International

* The International CAS registry number’s final digit.

Check digit – International

* Modulo 10 check digits in credit card account numbers, calculated by the Luhn algorithm.

Check digit – International

**Also used in the Norwegian KID (customer identification number) numbers used in bank giros (credit transfer).

Check digit – International

* Last check digit in EAN/UPC serialisation of Global Trade Identification Number (GTIN). It applies to GTIN-8, GTIN-12, GTIN-13 and GTIN-14.

Check digit – International

* The final digit of a Data Universal Numbering System|DUNS number (though this is scheduled to change, such as that the final digit will be chosen freely in new allocations, rather than being a check digit).

Check digit – International

* The third and fourth digits in an International Bank Account Number (Modulo 97 check).

Check digit – International

* The final character encoded in a magnetic stripe card is a computed Longitudinal redundancy check.

Check digit – In the USA

* The tenth digit of the National Provider Identifier for the US healthcare industry.

Check digit – In the USA

* The North American CUSIP number.

Check digit – In the USA

* The final (ninth) digit of the routing transit number, a bank code used in the United States.

Check digit – In the USA

* The ninth digit of a Vehicle identification number|Vehicle Identification Number (VIN).

Check digit – In the USA

* Mayo Clinic patient identification numbers used in Arizona and Florida include a trailing check digit.

Check digit – In Central America

* The Guatemalan Tax Number (NIT – Número de Identificación Tributaria) based on modulo operator|modulo 11.

Check digit – In Eurasia

* The Spanish fiscal identification number (número de identificación fiscal, NIF), (based on modulo 23).

Check digit – In Eurasia

* The ninth digit of an Israeli Teudat Zehut (Identity Card) number.

Check digit – In Eurasia

* The 13th digit of the Serbian and SFRY|Former Yugoslav Unique Master Citizen Number|Unique Master Citizen Number (JMBG).

Check digit – In Eurasia

* The last two digits of the 11-digit Turkish Identification Number ().

Check digit – In Eurasia

* The ninth character in the 14-character European Union|EU cattle passport number (cycles from 1 to 7: see British Cattle Movement Service#Ear tag number|British Cattle Movement Service).

Check digit – In Eurasia

* The ninth digit in an Icelandic Kennitala (national ID number).

Check digit – In Eurasia

* Modulo 97 check digits in a Belgium|Belgian and Serbian bank account numbers.

Check digit – In Eurasia

* The ninth digit in a Hungary|Hungarian TAJ number (social insurance number).

Check digit – In Eurasia

* For the residents of India, the unique identity number named AADHAAR#Salient features of AADHAAR|Aadhaar has a trailing 12th digit that is calculated with the Verhoeff algorithm.

Check digit – In Eurasia

* The Intellectual Property Office of Singapore|Intellectual Property Office of Singapore (IPOS) has confirmed a new format for application numbers of registrable Intellectual Property (IP, e.g., Trademark|trade marks, patents, Industrial design right|registered designs). It will include a check character calculated with the Damm algorithm.

Check digit – In Oceania

* The Australian Tax File Number (based on modulo operator|modulo 11).

Check digit – In Oceania

* The seventh character of a New Zealand NHI Number.

Check digit – In Oceania

* The last digit in a Locomotives of New Zealand|New Zealand locomotive’s Traffic Monitoring System (TMS) number.

Check digit – Algorithms

Notable algorithms include:

Luhn algorithm – Verification of the check digit

digits = digits_of(card_number)

Luhn algorithm – Calculation of the check digit

The algorithm above checks the validity of an input with a check digit. Calculating the check digit requires only a slight adaptation of the algorithm—namely:

Luhn algorithm – Calculation of the check digit

# Append a zero check digit to the partial number and calculate checksum

ISBN – ISBN-10 check digits

The 2001 edition of the official manual of the [http://www.isbn-international.org/ International ISBN Agency] says that the ISBN-10 check digit– which is the last digit of the ten-digit ISBN– must range from 0 to 10 (the symbol X is used for 10), and must be such that the sum of all the ten digits, each multiplied by its (integer) weight, descending from 10 to 1, is a multiple of 11 (number)|11.

ISBN – ISBN-10 check digits

The two most common errors in handling an ISBN (e.g., typing or writing it) are a single altered digit or the transposition of adjacent digits. It can be mathematical proof|proved that all possible valid ISBN-10’s have at least two digits different from each other. It can also be proved that there are no pairs of valid ISBN-10’s with eight identical digits and two transposed digits. (These are true only because the ISBN is less than 11 digits long, and because 11 is a prime number.)

ISBN – ISBN-10 check digits

The ISBN check digit method therefore ensures that it will always be possible to detect these two most common types of error, i.e

ISBN – ISBN-10 check digit calculation

The resulting remainder, plus the check digit, must equal 11; therefore, the check digit is (11 minus the remainder of the sum of the products modulo 11) modulo 11

ISBN – ISBN-10 check digit calculation

The value x_ required to satisfy this condition might be 10; if so, an ‘X’ should be used.

ISBN – ISBN-13 check digit calculation

The 2005 edition of the International ISBN Agency’s official manual describes how the 13-digit ISBN check digit is calculated. The ISBN-13 check digit, which is the last digit of the ISBN, must range from 0 to 9 and must be such that the sum of all the thirteen digits, each multiplied by its (integer) weight, alternating between 1 and 3, is a multiple of 10 (number)|10.

ISBN – ISBN-13 check digit calculation

The ISBN-10 formula uses the prime number|prime modulus 11 which avoids this blind spot, but requires more than the digits 0-9 to express the check digit.

Routing transit number – Check digit

The ninth, check digit provides a checksum test using a position-weighted sum of each of the digits. High-speed check-sorting equipment will typically verify the checksum and if it fails, route the item to a reject pocket for manual examination, repair, and re-sorting. Mis-routings to an incorrect bank are thus greatly reduced.

Routing transit number – Check digit

: (Modulo operation|Mod or modulo is the remainder of a division operation.)

Routing transit number – Check digit

In terms of weights, this is 371 371 371

Routing transit number – Check digit

As an example, consider 111000025 (which is a valid routing number of Bank of America in Virginia). Applying the formula, we get:

Routing transit number – Check digit

The following formula can be used to generate the ninth digit in the checksum:

Routing transit number – Check digit

This is just moving all terms other than d_9 to the right hand side of the equation, which inverts the coefficients with respect to 10 (3 \mapsto (10-3) = 7; 7 \mapsto (10-7) = 3; 1 \mapsto (10-1) = 9).

Routing transit number – Check digit

Following the above example for the Bank of America routing number 111000025,

Routing transit number – Check digit

This checksum is very easy to represent in computer programming languages. The following Python (programming language)|Python example will print True when the checksum is valid:

Damm algorithm – Validating a number against the included check digit

#Set up an interim digit and initialize it to 0.

Damm algorithm – Validating a number against the included check digit

#Process the number digit by digit: Use the number’s digit as column index and the interim digit as row index, take the table entry and replace the interim digit with it.

Damm algorithm – Validating a number against the included check digit

#The number is valid if and only if the resulting interim digit has the value of 0.

Damm algorithm – Calculating the check digit

The resulting interim digit is ‘4’. This is the calculated check digit. We append it to the number and obtain ‘5724’.

UPC code – Check digits

# Adding the odd-numbered digits (0+6+0+2+1+5 = 14)

UPC code – Check digits

# Calculating modulo ten (58mod10 = 8)

UPC code – Check digits

# Subtracting from ten (10minus;8 = 2)

Base 11 – ISBN check digit

The check digit for ISBN|ISBN-10 is found as the result of taking modular arithmetic|modulo 11. Since this could give 11 possible results, the digit X, not A, is used in place of 10. Remember that X (disambiguation)|X is the Roman numeral for ten. (The newer ISBN-13 standard uses modulo 10, so no extra digits are required.)

Code 128 – Check digit calculation

The remainder of the division is the check digit’s ‘value’ which is then converted into a character (following the instructions given Code 128#Conversion to char|below) and appended to the end of the barcode.

Code 128 – Calculating check digit with multiple variants

As Code 128 allows multiple variants, as well as switching between variants within a single barcode, the absolute Code 128 value of a character is completely independent of its value within a given variant. For instance the Variant C value 33 and the Variant B value A are both considered to be a Code 128 value of 33, and the check digit would be computed based on the value of 33 times the character’s position within the barcode.

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Call Center

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Call Center

Home Shopping Network – Call center

The Rockwell corporation’s Galaxy line of switches was used for the current Call Center (as well as the new locations in St

Home Shopping Network – Call center

HSN has an in-house Call Center in St. Petersburg, Florida, which mostly handles customer service calls. HSN also employs several hundred customer service representatives from work at home positions who take calls and place orders via HSN’s customer service intranet. HSN also contracts Call Centers to handle its sales calls especially when HSN is broadcasting shows with highly popular items.

Payment Card Industry Data Security Standard – PCI compliance in call centers

While the PCI DSS standards are very explicit about the requirements for the back end storage and access of PII (personally identifiable information), the Payment Card Industry Security Standards Council has said very little about the collection of that information on the front end, whether through websites, interactive voice response systems or call center agents. This is surprising, given the high threat potential for Creditcard fraud and data compromise that call centers pose.

Payment Card Industry Data Security Standard – PCI compliance in call centers

Home-based telephone agents pose an additional level of challenges, requiring the company to secure the channel from the home-based agent through the call center hub to the retailer applications.

Payment Card Industry Data Security Standard – PCI compliance in call centers

To address some of these concerns, on January 22, 2010 the Payment Card Industry Security Standards Council issued a revised FAQ about call center recordings. The bottom line is that companies can no longer store digital recordings that include CVV information if those recordings can be queried.

Payment Card Industry Data Security Standard – PCI compliance in call centers

This ensures seamless integration with the call center environment, with minimal disruption to agents, or current IT systems, whilst also reducing risk by enabling rapid implementation

Elastix – Call center module

Elastix was the first distribution that included a call center module with a predictive dialer, released entirely as free software. This module can be installed from the same web-based Elastix interface through a module loader. The call center module can handle incoming and outgoing campaigns.

Call center industry in the Philippines

‘Call Centers’ began in the Philippines as plain providers of email response and managing services, these have industrial capabilities for almost all types of customer relations, ranging from travel services, technical support, education, customer care, financial services, and online business-to-customer support, online business-to-business support. The Call Center industry is one of the fastest growing industries in the country.

Call center industry in the Philippines – Types of support

The calls managed by a number of Philippine Call Centers can be categorized into one of two types: outbound calls and inbound calls. Outbound calls include advisories, sales verification, customer services, surveys, collections and telemarketing. Inbound Calls include account inquiries, technical support, sales and various customer services.

Call center industry in the Philippines – Recruitment and training process

The recruitment process for new Call Center agents may include (but is not limited to) the following:

Call center industry in the Philippines – Recruitment and training process

There are various ways in which one may initiate a career in Call Centers. The most common of which is to apply directly to a Call Center’s recruitment office. This process is commonly coined as a walk-in application. Another procedure includes an employee referral, where an applicant is referred by an existing employee of a Call Center. A person may also apply through an employment agency, which will conduct its own screening procedures, before endorsing an applicant to any Call Center.

Call center industry in the Philippines – Recruitment and training process

An emerging manner to apply for a career in a Call Center is through online application, as it provides applicants with an easier way of acquiring more information on the Call Center or business, an easier application and resume submission and allows Filipinos in more far or remote areas to apply.

Call center industry in the Philippines – Number of centers

According to the Call Center Directory of the Philippine Economic Zone Authority (PEZA), the Philippines now has more than 1000 call centers over 20 key locations:

Call center industry in the Philippines – Outsourcing

Call Center managers require new hires to be extremely fluent in English and (for technical accounts) possess above-par Information technology|IT skills. Although some people would on the basis of experience with Call Centers dispute these requirements.

Call center industry in the Philippines – Outsourcing

The global recession in 2008 resulted in the loss of jobs for many Overseas Filipino Workers (OFWs). This prompted the Philippine government to assist OFWs transition to Call Center agents.http://www.articlearchives.com/labor-employment/labor-regulation-policy-labor-departments/216819-1.html The government program, funded by the Overseas Workers Welfare Administration (OWWA), is part of the government’s vocational scholarship program of OWWA and reintegration for OFWs returning to the country.

Business process outsourcing in the Philippines – Call Center Industry in the Philippines

In the Philippines, Call Centers began as providers of business email response and managing services. The Call Center sector comprises 80% of the total BPO industry in the country. With 80% of the call services provided for the US market.

Business process outsourcing in the Philippines – Call Center Industry in the Philippines

In 2008, Call Centers supported a $12-billion BPO industry.

Call center industry in Bangladesh

The ‘call center industry in Bangladesh’ was worth around $12 million in 2013, of which 50 percent was accounted for by the country’s domestic market. In 2013, national mobile operators Airtel and Citycell outsourced their call centers to local companies. Bangladesh Telecommunication Regulatory Commission (BTRC) eased the licensing process for call centers in 2013.

Call center industry in Bangladesh

A call center village was planned in 2009. , around 70 call centers were in operation in Bangladesh.Bangladesh exports its call center services to countries including the United States, Canada and the United Kingdom. The Bangladesh Association of Call Center Outsourcing (BACCO) was formed in connection with the industry.

Charter Communications – Call centers

On May 2, 2006, Charter announced it would restructure seven of its Call Centre|Call Centers in the United States in the following locations:

Charter Communications – Call centers

* St. Louis, Missouri— Telephone Care Center, July 31, 2006; converted into a Charter Phone service Call Center

Charter Communications – Call centers

Orders completed online or through retail partners with Charter Communication are directed to a Call Center located in Tempe, Arizona, operated by Teletech (Direct Alliance). This Call Center has inbound/outbound sales agents, as well as online chat agents. Outsourced Call Centers were implemented in 2006 and are located in Canada and the Philippines.

Charter Communications – Call centers

Louis, Missouri (telephone service support center); Greenville, South Carolina|Greenville, South Carolina; Vancouver, Washington; Fond du Lac, Wisconsin; Walker, Michigan; Rochester, Minnesota; Worcester, Massachusetts, and Louisville, Kentucky, (the largest Call Center across the company), with Heathrow, Florida, handling the bulk of video, high-speed data, and telephone billing and customer service contacts

International Merchant Services – Closing of the Kearney Call Center

First National has operated the call center in Kearney since 1992.

Service desk – Differences from a call center, contact center, help desk

ITIL regards a Call Center, Contact Centre (business)|Contact Center or a help desk as limited kinds of service desk which provide only a portion of what a service desk can offer

Chargeback fraud – Call center transactions

Another common channel for chargebacks is mail order/telephone order (MOTO) payment processing through a call center. In this case, as with the two others listed here, the main problem is that this is a card not present transaction. To help eliminate call center purchase chargebacks, call centers are working to make the purchases more like card present purchases.

Chargeback fraud – Call center transactions

Agent-assisted automation technology is available for call centers that allows customers to enter their credit card information, including the card security code directly into the customer relationship management software without the agent ever seeing or hearing it. The agent remains on the phone, so there is no awkward transfer to an interactive voice response system. All the agent can hear is monotones. This is the card present equivalent of swiping the card.

Ceedo – Ceedo for Call Center

Ceedo for Call Center is a variant of Ceedo Enterprise specially configured and tweaked for Call Center representatives working from home, ensuring complete monitoring and usage oversight, host minimum requirements compatibility checks, and other components that ensure high-quality VoIP and enterprise-level secure connections.Ceedo for Call Center product page: http://www.ceedo.com/products/ceedo-for-call-center.html

Ceedo – Ceedo for Call Center

Ceedo for Call Center is usually locally installed on the user’s computer, and is employed along with various security mechanisms and Unified Communications related software.

PCI DSS – PCI compliance in call centers

Home-based telephone agents pose an additional level of challenges, requiring the company to secure the channel from the home-based agent through the call center hub to the retailer applications.

PCI DSS – PCI compliance in call centers

To address some of these concerns, on January 22, 2010 the Payment Card Industry Security Standards Council issued a revised FAQ about call center recordings. The bottom line is that companies can no longer store digital recordings that include Card Verification Value|CVV information if those recordings can be queried.

PCI DSS – PCI compliance in call centers

This ensures seamless integration with the call center environment, with minimal disruption to agents, or current IT systems, whilst also reducing risk by enabling rapid implementation

Teletraffic engineering – In call centers

A good example of the use of teletraffic theory in practice is in the design and management of a call center. Call centers use teletraffic theory to increase the efficiency of their services and overall profitability through calculating how many operators are really needed at each time of the day.

Teletraffic engineering – In call centers

Queueing systems used in call centers have been studied as a science. For example completed calls are put on hold and queued until they can be served by an operator. If callers are made to wait too long, they may lose patience and default from the queue (hang up), resulting in no service being provided.

Call center industry – Recruitment and training process

There are various ways in which one may initiate a career in call centers, the most common of which is to apply directly to a call center’s recruitment office. This process is commonly coined as a walk-in application. Another procedure includes an employee referral, where an applicant is referred by an existing employee of a call center. A person may also apply through an employment agency, which will conduct its own screening procedures, before endorsing an applicant to any call center.

Bonus-Malus – Call centers

Bonus-malus payments are in addition to the normal cost of call center services.

Call center security

‘Information security’ has emerged as a significant concern for banks, mobile phone companies and other businesses that use call centers or business process outsourcing, or ‘BPO’. There have been instances of theft of personal data reported from call centers.

Call center security – Common countermeasures

There are three identifiable types of illicit activities concerning fraud emanating from call centers:

Call center security – Common countermeasures

*5. Limiting functionality and access of personal computers or terminals used by call center agents (for example, disabling USB ports). Companies may also use data loss prevention software to block attempts to download, copy, or transmit sensitive electronic data.[http://www.csoonline.com/article/356064 Call Center Security How: to Protect Employees and Customers]

Härnösand – Call centers

One of the biggest employers in Härnösand is the cable-TV and Cable internet service provider Com Hem. Com Hem in Härnösand takes care of all incoming support calls and therefore hire mostly young adults with an interest in technology. The Interview Institute of Scandinavia and Intervjubolaget are also employers seated in Härnösand focusing on call center activities, although their focus lies in doing interviews by phone or face to face.

One Night @ the Call Center

The novel revolves around a group of six call center employees working at the Connexions call center in Gurgaon, Haryana

One Night @ the Call Center – Plot

One night they got a phone-call from god within the story, which comprises the bulk of the book, relates the events that happen one night at a call center

One Night @ the Call Center – Plot

Claimed to be based on a true story, the author chooses Shyam Mehra (pseudonym|alias Sam Marcy) as the narrator and protagonist, who is one among the six call center employees featured.

One Night @ the Call Center – Plot

On returning to the call center, they carry out their plans with dexterity.

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Binary Digit

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Binary Digit

Bit

A bit is the basic unit of information in computing and digital communications. A bit can have only one of two values, and may therefore be physically implemented with a two-state device. The most common representation of these values are 0and1. The term bit is a contraction of binary digit.

Bit History

Tukey, who had written a Bell Labs memo on 9 January 1947 in which he contracted “binary digit” to simply “bit”

Bit Unit and symbol

The bit is not defined in the International System of Units (SI). However, the International Electrotechnical Commission issued standard IEC 60027, which specifies that the symbol for binary digit should be bit, and this should be used in all multiples, such as kbit, for kilobit. However, the lower-case letter b is widely used as well and was recommended by the IEEE 1541 Standard (2002). In contrast, the upper case letter B is the standard and customary symbol for byte.

Bit Information capacity and information compression

Using an analogy, the hardware binary digits refer to the amount of storage space available (like the number of buckets available to store things), and the information content the filling, which comes in different levels of granularity (fine or coarse, that is, compressed or uncompressed information)

Hexadecimal

Each hexadecimal digit represents four binary digits (bits), and the primary use of hexadecimal notation is a human-friendly of binary-coded values in computing and digital electronics. One hexadecimal digit represents a nibble, which is half of an octet or byte (8 bits). For example, byte values can range from 0 to 255 (decimal), but may be more conveniently represented as two hexadecimal digits in the range 00 to FF. Hexadecimal is also commonly used to represent computer memory addresses.

Octal

The octal amount system, or oct for short, is the base-8 number system, and uses the digits 0 to 7. Octal numerals can be made from binary numerals by grouping consecutive binary digits into groups of three (starting from the right). For example, the binary representation for decimal 74 is 1001010, which can be grouped into (00)1 001 010 – so the octal representation is 112.

Octal In computers

Octal was an ideal abbreviation of binary for these machines because their word size is divisible by three (each octal digit represents three binary digits)

Octal In computers

On such systems three octal digits per byte would be required, with the most significant octal digit representing two binary digits (plus one bit of the next significant byte, if any)

Octal Binary to octal conversion

The process is the reverse of the previous algorithm. The binary digits are grouped by threes, starting from the least significant bit and proceeding to the left and to the right. Add leading 0s (or trailing zeros to the right of decimal point) to fill out the last group of three if necessary. Then replace each trio with the equivalent octal digit.

Binary number History

In 1605 Francis Bacon discussed a system whereby letters of the alphabet could be reduced to sequences of binary digits, which could then be encoded as scarcely visible variations in the font in any random text

Binary number Representation

Any number can be represented by any sequence of bits (binary digits), which in turn may be represented by any mechanism capable of being in two mutually exclusive states. The following sequence of symbols could all be interpreted as the binary numeric value of 667:

Binary number Counting in binary

Since binary is a base-2 system, each digit represents an increasing power of 2, with the rightmost digit representing 20, the next representing 21, then 22, and so on. To determine the decimal representation of a binary number simply take the sum of the products of the binary digits and the powers of 2 which they represent. For example, the binary number 100101 is converted to decimal form as follows:

Binary number Hexadecimal

To convert a hexadecimal number into its binary equivalent, simply substitute the corresponding binary digits:

Binary number Octal

Binary is also easily converted to the octal amount system, since octal uses a radix of 8, which is a power of two (namely, 23, so it takes exactly three binary digits to represent an octal digit). The correspondence between octal and binary numerals is the same as for the first eight digits of hexadecimal in the table above. Binary 000 is equivalent to the octal digit 0, binary 111 is equivalent to octal 7, and so forth.

Boolean algebra Values

Whereas in elementary algebra expressions denote mainly numbers, in Boolean algebra they denote the truth values false and true. These values are represented with the bits (or binary digits) being 0 and 1. They do not behave like the integers 0 and 1, for which 1 + 1 = 2, but may be identified with the elements of the two-element field GF(2), for which 1 + 1 = 0 with + serving as the Boolean operation XOR.

Operating system – History

After programmable general purpose computers were invented, machine languages (consisting of strings of the binary digits 0 and 1 on punched paper tape) were introduced that sped up the programming process (Stern, 1981).

Central processing unit – Integer range

For example, an 8-bit CPU deals with a range of numbers that can be represented by eight binary digits (each digit having two possible values), that is, 28 or 256 discrete numbers

Computer data storage – Data organization and representation

Text, numbers, pictures, audio, and nearly any other form of information can be converted into a string of bits, or binary digits, each of which has a value of 1 or 0

Integer (computer science)

Integers are commonly represented in a computer as a group of binary digits

Entropy (information theory) – Entropy as information content

Shannon’s definition of entropy, when applied to an information source, can determine the minimum channel capacity required to reliably transmit the source as encoded binary digits (see caveat below in italics). The formula can be derived by calculating the mathematical expectation of the amount of information contained in a digit from the information source. See also Shannon-Hartley theorem.

Quantum computer

Whereas digital computers require data to be encoded into binary digits (bits), Quantum Computation uses quantum properties to represent data and perform operations on these data

Digital data – Historical digital systems

More recently invented, a modem modulates an analog “carrier” signal (such as sound) to encode binary electrical digital information, as a series of binary digital sound pulses. A slightly earlier, surprisingly reliable version of the same concept was to bundle a sequence of audio digital “signal” and “no signal” information (i.e. “sound” and “silence”) on magnetic cassette tape for use with early home computers.

Hardware random number generator

A hardware random number generator typically consists of a transducer to convert some aspect of the physical phenomena to an electrical signal, an amplifier and other electronic circuitry to increase the amplitude of the random fluctuations to a macroscopic level, and some type of analog to digital converter to convert the output into a digital number, often a simple binary digit 0 or 1

Manchester Small-Scale Experimental Machine – Background

Konrad Zuse’s Z3 was the world’s first working programmable, fully automatic computer, with binary digital arithmetic logic, but it lacked the conditional branching of a Turing machine

Manchester Small-Scale Experimental Machine – Williams-Kilburn tube

For use in a binary digital computer, the tube had to be capable of storing either one of two states at each of its memory locations, corresponding to the binary digits (bits) 0 and 1

Computer

Inside each of these parts are thousands to trillions of small electrical circuits which can be turned off or on by means of an electronic switch. Each circuit represents a bit (binary digit) of information so that when the circuit is on it represents a “1”, and when off it represents a “0” (in positive logic representation). The circuits are arranged in logic gates so that one or more of the circuits may control the state of one or more of the other circuits.

Data type – Boolean type

The Boolean type represents the values: true and false. Although only two values are possible, they are rarely implemented as a single binary digit for efficiency reasons. Many programming languages do not have an explicit boolean type, instead interpreting (for instance) 0 as false and other values as true.

Ferranti Mark 1 – History and specifications

The engineers decided to use the simplest mapping between the paper holes and the binary digits they represented, but the mapping between the holes and the physical keyboard was never meant to be a binary mapping

Ferranti Mark 1 – Mark 1 Star

Instead of the original mapping from holes to binary digits that resulted in the random-looking mapping, the new machines mapped digits to holes in order to produce a much simpler mapping, ø£½0@:$ABCDEFGHIJKLMNOPQRSTUVWXYZ

Digital electronics – Advantages

An advantage of digital circuits when compared to analog circuits is that signals represented digitally can be transmitted without degradation due to noise. For example, a continuous audio signal transmitted as a sequence of 1s and 0s, can be reconstructed without error, provided the noise picked up in transmission is not enough to prevent identification of the 1s and 0s. An hour of music can be stored on a compact disc using about 6 billion binary digits.

Digital electronics – Advantages

In a digital system, a more precise representation of a signal can be obtained by using more binary digits to represent it. While this requires more digital circuits to process the signals, each digit is handled by the same kind of hardware. In an analog system, additional resolution requires fundamental improvements in the linearity and noise characteristics of each step of the signal chain.

Digital electronics – Structure of digital systems

A sequential system is a combinational system with some of the outputs fed back as inputs. This makes the digital machine perform a “sequence” of operations. The simplest sequential system is probably a flip flop, a mechanism that represents a binary digit or “bit”.

Consensus (computer science) – Models of computation

A special case of the consensus problem called binary consensus restricts the input and hence the output domain to a single binary digit {0,1}. When the input domain is large relative to the number of processes, for instance an input set of all the natural numbers, it can be shown that consensus is impossible in a synchronous message passing model.

Binary data

* bit (binary digit) in computer science,

Computing Machinery and Intelligence – Digital machines

Turing also notes that we need to determine which machines we wish to consider. He points out that a human cloning|clone, while man-made, would not provide a very interesting example. Turing suggested that we should focus on the capabilities of digital machinery—machines which manipulate the binary digits of 1 and 0, rewriting them into memory using simple rules. He gave two reasons.

Computer & Video Games – Components

Inside each of these parts are thousands to trillions of small electrical network|electrical circuits which can be turned off or on by means of an transistor|electronic switch. Each circuit represents a bit (binary digit) of information so that when the circuit is on it represents a “1”, and when off it represents a “0” (in positive logic representation). The circuits are arranged in logic gates so that one or more of the circuits may control the state of one or more of the other circuits.

Binary code

A ‘binary code’ represents Plain text|text or Instruction set|computer processor instructions using the binary number|binary number system’s two binary Numerical digit|digits, 0 and 1. A binary code assigns a bit string to each symbol or instruction. For example, a binary String (computer science)|string of eight binary digits (bits) can represent any of 256 possible values and can therefore correspond to a variety of different symbols, letters or instructions.

Operating systems – History

After programmable general purpose computers were invented, machine languages (consisting of strings of the binary digits 0 and 1 on punched paper tape) were introduced that sped up the programming process (Stern, 1981).

CPU – Integer range

For example, an 8-bit CPU deals with a range of numbers that can be represented by eight binary digits (each digit having two possible values), that is, 28 or 256 discrete numbers

Binaries – Structure

Binary files are usually thought of as being a sequence of bytes, which means the binary digits (bits) are grouped in eights

Digital media

There is a rich history of Numeral system|non-binary digital media, computers, and their rise to prominence over the last couple decades.

Philosophy of artificial intelligence – Consciousness, minds, mental states, meaning

The difficult philosophical question is this: can a computer program, running on a digital machine that shuffles the binary digits of zero and one, duplicate the ability of the neural correlates of consciousness|neurons to create minds, with mental states (like understanding or perceiving), and ultimately, the experience of consciousness?

Transmitter – How it works

*A modulator circuit to add the information to be transmitted to the carrier wave produced by the oscillator. This is done by varying some aspect of the carrier wave. The information is provided to the transmitter either in the form of an audio signal, which represents sound, a video signal, or for data in the form of a Binary numeral system|binary Digital data|digital signal.

Transmitter – How it works

**In an FSK (frequency-shift keying) transmitter, which transmits digital data, the frequency of the carrier is shifted between two frequencies which represent the two binary digits, 0 and 1.

Quartz clock – Explanation

A 15-bit Binary numeral system|binary Digital electronics|digital counter driven by the frequency will overflow once per second, creating a digital pulse once per second

Compound word – Recent trends

Although there is no universally agreed-upon guideline regarding the use of compound words in the English language, in recent decades written English has displayed a noticeable trend towards increased use of compounds. Recently, many words have been made by taking syllables of words and compounding them, such as pixel (picture element) and bit (binary digit). This is called a syllabic abbreviation.

RGB color model – Color depth

The RGB color model is the most common way to encode color in computing, and several different binary numeral system|binary Digital data|digital representations are in use

Atanasoff–Berry Computer – Design and construction

#Using binary numeral system|binary digits to represent all numbers and data

Computer storage – Data organization and representation

Text, numbers, pictures, audio, and nearly any other form of information can be converted into a string of bits, or binary digits, each of which has a value of 1 or 0

128-bit

However, these processors do not operate on individual numbers that are 128 binary digits in length, only their Processor register|registers have the size of 128-bits.

256-bit

However, these processors do not operate on individual numbers that are 256 binary digits in length, only their Processor register|registers have the size of 256-bits.

QPSK

PSK uses a finite number of phases, each assigned a unique pattern of bit|binary digits

WWVB – History

A time code was added to WWVB on July 1, 1965. This made it possible for clocks to be designed that could receive the signal, decode it, and then automatically synchronize themselves. The time code format has changed only slightly since 1965; it uses a scheme known as binary coded decimal (BCD) which uses four binary digits (bits) to send each decimal digit.

IEEE 754-1985 – Denormalized numbers

The number representations described above are called normalized, meaning that the implicit leading binary digit is a 1

Punched card – IBM 80-column punched card formats and character codes

For some computer applications, Binary numeral system|binary formats were used, where each hole represented a single binary digit (or bit), every column (or row) was treated as a simple bitfield, and every combination of holes was permitted

Quantum computing

Whereas digital computers require data to be encoded into binary digits (bits), quantum computation uses quantum properties to represent data and perform Instruction (computer science)|operations on these data.[http://phm.cba.mit.edu/papers/98.06.sciam/0698gershenfeld.html Quantum Computing with Molecules] article in Scientific American by Neil Gershenfeld and Isaac L

DSL – History

A DSL circuit terminates at each end in a modem which modulates patterns of Binary digit|bits into certain high-frequency impulses for transmission to the opposing modem

Digital physics – Overview

Some try to identify single physical particles with simple Binary digit|bits

Stream cipher – Synchronous stream ciphers

In a ‘synchronous stream cipher’ a stream of pseudo-random digits is generated independently of the plaintext and ciphertext messages, and then combined with the plaintext (to encrypt) or the ciphertext (to decrypt). In the most common form, binary digits are used (bits), and the keystream is combined with the plaintext using the exclusive or operation (XOR). This is termed a ‘binary additive stream cipher’.

Digitizer – Process

The term digitization is often used when diverse forms of information, such as text, sound, image or voice, are converted into a single binary code. Digital information exists as one of two digits, either 0 or 1. These are known as bits (a contraction of binary digits) and the sequences of 0s and 1s that constitute information are called bytes.Flew, Terry. 2008. New Media An Introduction. South Melbourne. 3rd Edition. South Melbourne: Oxford University Press.

History of cryptography – Claude Shannon

In proving “perfect secrecy”, Shannon determined that this could only be obtained with a secret key whose length given in binary digits was greater than or equal to the number of bits contained in the information being encrypted

State (computer science) – Finite state machines

Since each binary digit|binary memory element has only two possible states, 0 or 1, the total number of different states a circuit can assume is finite, and fixed by the number of memory elements

CDC 6000 series – Central memory

Information is stored in central memory in the form of words. The length of each word is 60 binary digits (bits). The highly efficient address and data control mechanisms involved permit a word to be moved into or out of central memory up to one every 100 nanoseconds.

Connected Component Analysis

Connected-component labeling is used in computer vision to detect connected regions in binary image|binary digital images, although color images and data with higher dimensionality can also be processed

Carry-save adder – Motivation

In electronic terms, using bits (binary digits), this means that even if we have n one-bit adders at our disposal, we still have to allow a time proportional to n to allow a possible carry to propagate from one end of the number to the other. Until we have done this,

The Magical Number Seven, Plus or Minus Two – Miller’s article

He noticed that memory span is approximately the same for stimuli with vastly different amount of information—for instance, binary digits have 1 bit each; decimal digits have 3.32 bits each; words have about 10 bits each

Sense amplifier

The job of a sense amplifier is to sense the low power signals from a bitline which represents a data binary digit|bit (1 or 0) stored in a Computer data storage|memory cell, and amplify the small voltage swing to recognizable logic levels so the data can be interpreted properly by logic outside the memory.A Low-Power SRAM Using Bit-Line Charge-Recycling for Read and Write Operations[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=arnumber=5584956contentType=Journals+%26+MagazinesqueryText%3Dread+write+operation+of+SRAM], IEEE Journal of Solid-State Circuits, 2010 IEEE Modern sense-amplifier circuits consist of 2 to 6 (usually 4) transistors

Classical information – Quantifying classical physical information

If the log is taken base 2, the unit of information is the binary digit or bit (so named by John Tukey); if we use a natural logarithm instead, we might call the resulting unit the nat

Accuracy

In numerical analysis, accuracy is also the nearness of a calculation to the true value; while precision is the resolution of the representation, typically defined by the number of decimal or binary digits.

Cipher – Key size and vulnerability

An example of this process can be found at [http://www.keylength.com/ Key Length] which uses multiple reports to suggest that a symmetric cipher with 128 binary digit|bits, an asymmetric cipher with 3072 bit keys, and an Elliptic curve cryptography|elliptic curve cipher with 512 bits, all have similar difficulty at present.

Baud – Relationship to gross bit rate

The term baud has sometimes incorrectly been used to mean bit rate, since these rates are the same in old modems as well as in the simplest digital communication links using only one bit per symbol, such that binary 0 is represented by one symbol, and binary 1 by another symbol. In more advanced modems and data transmission techniques, a symbol may have more than two states, so it may represent more than one bit (a bit (binary digit) always represents one of exactly two states).

RSA Factoring Challenge

The first RSA numbers generated, RSA-100 to RSA-500 and RSA-617, were labeled according to their number of decimal digits; the other RSA numbers (beginning with RSA-576) were generated later and labelled according to their number of binary numeral system|binary digits.

Symbol rate – Symbols

There may be a direct correspondence between a symbol and a small unit of data (for example, each symbol may Encoding|encode one or several binary digits or ‘bits’) or the data may be represented by the transitions between symbols or even by a sequence of many symbols

Symbol rate – Relationship to gross bitrate

In more advanced modems and data transmission techniques, a symbol may have more than two states, so it may represent more than one binary digit (a binary digit always represents one of exactly two states)

Symbol rate – Relationship to gross bitrate

It takes three Binary numeral system|binary digits to encode eight states

ADSL modem – Data transmission

The binary digit|bits of the incoming digital data are split up and sent in parallel over the channels

Chunking (psychology) – Magic number seven

With sufficient drill, people found it possible to remember as many as forty binary digits

Chunking (psychology) – Magic number seven

It is a little dramatic to watch a person get 40 binary digits in a row and then repeat them back without error. However, if you think of this merely as a mnemonic trick for extending the memory span, you will miss the more important point that is implicit in nearly all such mnemonic devices. The point is that recoding is an extremely powerful weapon for increasing the amount of information that we can deal with.

History of computer science – Alan Turing and the Turing Machine

Each cell contains a binary digit, 1 or 0

Digital technology – Advantages

An hour of music can be stored on a compact disc using about 6 billion binary digits.

Digital technology – Advantages

In a digital system, a more precise representation of a signal can be obtained by using more binary digits to represent it. While this requires more digital circuits to process the signals, each digit is handled by the same kind of hardware. In an analog system, additional resolution requires fundamental improvements in the linearity and noise characteristics of each step of the Signal chain (signal processing chain)|signal chain.

Pioneer plaque – Hyperfine transition of neutral hydrogen

Below this symbol is a small vertical line to represent the binary numeral system|binary digit 1

Approximation – Mathematics

The results of computer calculations are normally an approximation expressed in a limited number of significant digits, although they can be programmed to produce more precise results.[http://docs.oracle.com/cd/E19957-01/806-3568/ncg_goldberg.html Numerical Computation Guide] Approximation can occur when a decimal number cannot be expressed in a finite number of binary digits.

Cantor’s diagonal argument – An uncountable set

In his 1891 article, Cantor considered the set T of all infinite sequences of binary digits (i.e. consisting only of 0es and 1s).

Arecibo message

The message consisted of 1,679 bit|binary digits, approximately 210 bytes, transmitted at a frequency of 2,380hertz|MHz and modulated by shifting the frequency by 10Hz, with a power of 1,000kilowatt|kW

Arecibo message – Numbers

To read the first seven digits, ignore the bottom row, and read them as three binary digits from top to bottom, with the top digit being the most significant

RF front end

In many modern integrated receivers, particularly those in wireless devices such as cell phones and Wifi receivers, the intermediate frequency is digitized; sampled and converted to a binary number|binary digital signal|digital form, and the rest of the processing – IF filtering and demodulation – is done by digital filters (digital signal processing, DSP), as these are smaller, use less power and can have more selectivity

Amplifiers – Class D

Another advantage of the class-D amplifier is that it can operate from a digital signal source without requiring an digital-to-analog converter (DAC) to convert the signal to analog form first. If the signal source is in digital form, such as in a digital media player or computer sound card, the digital circuitry can convert the binary digital signal directly to a pulse width modulation signal to be applied to the amplifier, simplifying the circuitry considerably.

Arithmetic underflow – The underflow gap

point datatype can represent 20 binary digits, the underflow gap is 221 times

Finger binary – Mechanics

It is possible to use digit (anatomy)|anatomical digits to represent numerical digits by using a raised finger to represent a binary digit in the 1 state and a lowered finger to represent it in the 0 state. Each successive finger represents a higher power of two.

Hollerith cards – IBM 80-column punched card formats and character codes

For some computer applications, Binary numeral system|binary formats were used, where each hole represented a single binary digit (or bit), every column (or row) was treated as a simple bitfield, and every combination of holes was permitted

List of algorithms – Elementary and special functions

** Bailey–Borwein–Plouffe formula: (BBP formula) a spigot algorithm for the computation of the nth binary digit of ?

Brute force search – Basic algorithm

If n is a random 64-binary digit|bit natural number, which has about 19 decimal digits on the average, the search will take about 10 years

Canonical Huffman code – As a fractional binary number

Another perspective on the canonical codewords is that they are the digits past the radix point (binary decimal point) in a binary representation of a certain series. Specifically, suppose the lengths of the codewords are l1 … ln. Then the canonical codeword for symbol i is the first li binary digits past the radix point in the binary representation of

Run-length encoding

Typical applications of this encoding are when the source information comprises long substrings of the same character or binary digit.

Karnaugh map

The row and column indices (shown across the top, and down the left side of the Karnaugh map) are ordered in Gray code rather than binary numerical order. Gray code ensures that only one variable changes between each pair of adjacent cells. Each cell of the completed Karnaugh map contains a binary digit representing the function’s output for that combination of inputs.

Numeral system – Main numeral systems

In computers, the main numeral systems are based on the positional system in base 2 (binary numeral system), with two binary digits, 0 and 1. Positional systems obtained by grouping binary digits by three (octal numeral system) or four (hexadecimal numeral system) are commonly used. For very large integers, bases 232 or 264 (grouping binary digits by 32 or 64, the length of the machine word) are used, as, for example, in GNU Multiple Precision Arithmetic Library|GMP.

Numeric precision in Microsoft Excel – Accuracy and binary storage

In short, a variety of accuracy behavior is introduced by the combination of representing a number with a limited number of binary digits, along with Truncation error|truncating numbers beyond the fifteenth significant figure.

Experimental mathematics – History

A significant milestone and achievement of experimental mathematics was the discovery in 1995 of the Bailey–Borwein–Plouffe formula for the binary digits of ?

Boolean equation – Values

Whereas in elementary algebra expressions denote mainly numbers, in Boolean algebra they denote the truth values false and true. These values are represented with the bits (or binary digits), namely 0 and 1. They do not behave like the integers 0 and 1, for which 1 + 1 = 2, but may be identified with the elements of the GF(2)|two-element field GF(2), for which 1 + 1 = 0 with + serving as the Boolean operation XOR.

Binary logarithm – Real number

Fortunately, in practice we can do the computation and know the error margin without doing any algebra or any infinite series truncation. Suppose we want to compute the binary log of 1.65 with four binary digits. Repeat these steps four times:

Decimal point – Exceptions to digit grouping

For example, APA style stipulates a thousands separator for most figures of 1,000 or more except for page numbers, binary digits, temperatures, etc.

RSA number

The first RSA numbers generated, from RSA-100 to RSA-500, were labeled according to their number of decimal digits. Later, beginning with RSA-576, binary numeral system|binary digits are counted instead. An exception to this is RSA-617, which was created before the change in the numbering scheme. The numbers are listed in increasing order below.

RSA number – RSA-1024

Successful factorization of RSA-1024 has important security implications for many users of the RSA (algorithm)|RSA public-key cryptography|public-key authentication algorithm, as the most common key length currently in use is 1024 Binary digit|bits.

Gray code

The ‘reflected binary code’, also known as ‘Gray code’ after Frank Gray (researcher)|Frank Gray, is a binary numeral system where two successive values differ in only one bit (binary digit).

Shorten (file format)

It is a form of lossless data compression|data compression of files and is used to Audio data compression|losslessly compress Compact Disc|CD-quality audio files (44.1 kilohertz|kHz 16-binary digit|bit stereo Pulse-code modulation|PCM)

Amplitude-shift keying

ASK uses a finite number of amplitudes, each assigned a unique pattern of bit|binary digits

Parallel communications

In telecommunication and computer science, ‘parallel communication’ is a method of conveying multiple binary digits (bits) simultaneously. It contrasts with serial communication, which conveys only a single bit at a time; this distinction is one way of characterizing a communications link.

Algorithmic information theory – Overview

Although the digits of ‘?’ cannot be determined, many properties of ‘?’ are known; for example, it is an algorithmically random sequence and thus its binary digits are evenly distributed (in fact it is normal number|normal).

Memory refresh

In a DRAM chip, each binary digit|bit of memory data is stored as the presence or absence of an electric charge on a small capacitor on the chip

Elliptic curve factorization

, it is still the best algorithm for divisors not greatly exceeding 20 to 25 decimal|digits (64 to 83 binary digit|bits or so), as its running time is dominated by the size of the smallest factor p rather than by the size of the number n to be factored

Algorithmic randomness

Intuitively, an ‘algorithmically random sequence’ (or ‘random sequence’) is an infinite Sequence#Infinite sequences in theoretical computer science|sequence of binary digits that appears random to any algorithm. The notion can be applied analogously to sequences on any finite alphabet (e.g. decimal digits). Random sequences are key objects of study in algorithmic information theory.

Algorithmic randomness

Because infinite sequences of binary digits can be identified with real numbers in the unit interval, random binary sequences are often called ‘random real numbers’. Additionally, infinite binary sequences correspond to characteristic functions of sets of natural numbers; therefore those sequences might be seen as sets of natural numbers.

Algorithmic randomness – Three equivalent definitions

* ‘Kolmogorov complexity’ (Schnorr 1973, Levin 1973): Kolmogorov complexity can be thought of as a lower bound on the algorithmic compressibility of a finite sequence (of characters or binary digits)

Measurement while drilling

These sensors, as well as any additional sensors to measure rock formation density, porosity, pressure or other data, are connected, physically and digitally, to a logic unit which converts the information into binary digits which are then transmitted to surface using mud pulse telemetry (MPT, a binary coding transmission system used with fluids, such as, combinatorial, Manchester encoding, split-phase, among others).

Place value – Base conversion

base-16 can be achieved by writing each group of four binary digits as one hexadecimal digit.)

Place value – Computing

In computing, the Binary numeral system|binary (base-2) and hexadecimal (base-16) bases are used. Computers, at the most basic level, deal only with sequences of conventional zeroes and ones, thus it is easier in this sense to deal with powers of two. The hexadecimal system is used as shorthand for binary—every 4 binary digits (bits) relate to one and only one hexadecimal digit. In hexadecimal, the six digits after 9 are denoted by A, B, C, D, E, and F (and sometimes a, b, c, d, e, and f).

Square-free integer – Encoding as binary numbers

e.g. The square-free number 42 has factorisation 2times;3times;7, or as an infinite product: 21·31 ·50·71·110·130·…; Thus the number 42 may be encoded as the binary sequence …001011 or 11 decimal. (Note that the binary digits are reversed from the ordering in the infinite product.)

Theil index – Formula

In information theory, when information is given in binary digits, k=1 and the log base is 2

OR gate

The ‘OR gate’ is a digital logic gate that implements logical disjunction – it behaves according to the truth table to the right. A HIGH output (1) results if one or both the inputs to the gate are HIGH (1). If neither input is high, a LOW output (0) results. In another sense, the function of OR effectively finds the maximum between two binary digits, just as the complementary AND function finds the minimum.

Significand – Significands and the hidden bit

When working in binary number system|binary, the significand is characterized by its width in bit|binary digits (bits)

AND gate

In another sense, the function of AND effectively finds the minimum between two binary digits, just as the OR gate|OR function finds the maximum

Semiprime – Applications

In 1974 the Arecibo message was sent with a radio signal aimed at a star cluster. It consisted of 1679 binary digits intended to be interpreted as a 23times;73 bitmap image. The number 1679 = 23times;73 was chosen because it is a semiprime and therefore can only be broken down into 23 rows and 73 columns, or 73 rows and 23 columns.

MSIN

MIN2 is the second part of the MIN containing the 10 most significant binary digits

List of binary codes

This is a list of some ‘binary codes’ that are (or have been) used to represent Plain text|text as a sequence of binary digits 0 and 1. Fixed-width binary codes use a set number of bits to represent each character in the text, while in variable-length code|variable-width binary codes, the number of bits may vary from character to character.

John W. Tukey – Statistical terms

While working with John von Neumann on early computer designs, Tukey introduced the word bit as a contraction of binary digit.[http://www.linfo.org/bit.html The origin of the ‘bit’] The term bit was first used in an article by Claude Shannon in 1948.

Memory address – Types of memory addresses

The memory controllers’ bus (computing)|bus consists of a number of parallel communication|parallel lines, each represented by a binary digit (bit)

Bit-length

Is the number of binary digits, called bits, necessary to represent an integer in the binary numeral system|binary number system.

Bit-length

For example, computer processors are often designed to process data group into data type|words of a given length of bits (8 bit, 16 bit, 32 bit, 64 bit, etc.). The bit-length of each word (data type)|word defines, for one thing, how many memory locations can be independently addressed by the processor. In public-key cryptography, key (cryptography)|keys are defined by their length expressed in binary digits – their bit length.

Units of information – Primary units

When b is 2, the unit is the Shannon (unit)|shannon, equal to the information content of one bit (a contraction of binary digit). A system with 8 possible states, for example, can store up to log28 = 3 bits of information. Other units that have been named include:

Comparison of analog and digital recording – Quantization

The range of possible values that can be represented numerically by a sample is defined by the number of binary digits used

List of Ben 10 aliens – Echo Echo

Echo Echo is a small white silicon-based alien whose body is a living amplifier, has a headphones connected to square appendage resembling an MP3 player in his back with a port on it decorated with a binary digit situated like the number 10

Clock of the Long Now – Time calculations

Instead, the clock uses binary digital logic, implemented mechanically in a sequence of stacked binary adders (or as their inventor, Hillis, calls them, serial bit-adders)

Digit extraction algorithm – Example

This example illustrates the working of a spigot algorithm by calculating the binary digits of the natural logarithm of 2 using the identity

Digit extraction algorithm – Example

To start calculating binary digits from, say, the 8th place we multiply this identity by 27(since 7 = 8 – 1):

Digit extraction algorithm – Example

so the 8th to 11th binary digits in the binary expansion of ln(2) are 1, 0, 1, 1. Note that we have not calculated the values of the first seven binary digits – indeed, all information about them has been intentionally discarded by using modular arithmetic in the head sum.

Digit extraction algorithm – Example

The precision (arithmetic)|precision of calculations and intermediate results and the number of terms taken from the tail sum are all independent of n, and only depend on the number of binary digits that are being calculated – single precision arithmetic can be used to calculate around 12 binary digits, regardless of the starting position.

Simon Plouffe

‘Simon Plouffe’ is a Quebec mathematician born on June 11, 1956 in :fr:Saint-Jovite|Saint-Jovite, Quebec. He discovered the formula for the BBP algorithm (the Bailey–Borwein–Plouffe formula) which permits the computation of the nth binary numeral system|binary digit of pi|pi;, in 1995. Plouffe is also the co-author of the Encyclopedia of Integer Sequences, made into the web site (On-Line Encyclopedia of Integer Sequences) dedicated to integer sequences later in 1995.

Infinite monkey theorem – Correspondence between strings and numbers

The infinitely long string thusly produced would correspond to the Binary numeral system|binary digits of a particular real number between 0 and 1

Bailey–Borwein–Plouffe formula

The ‘Bailey–Borwein–Plouffe formula’ (‘BBP formula’) is a spigot algorithm for computing the nth binary digit of ‘pi’ (symbol: ) using Hexadecimal| math

PiHex

* Binary digits of Pi from five trillion minus three to five trillion seventy-six (completed August 30, 1998):

PiHex

* Binary digits of Pi from forty trillion minus three to forty trillion sixty-four (February 9, 1999):

PiHex

* Binary digits of Pi from one quadrillion minus three to one quadrillion sixty (September 11, 2000):

Prime constant

The ‘prime constant’ is the real number \rho whose nth binary digit is 1 if n is Prime number|prime and 0 if n is Composite number|composite or 1.

Aaron Stone – Omega Defiance

His name is probably a reference to the binary code, zero being one of the binary digits.

RSA numbers – RSA-1024

Successful factorization of RSA-1024 has important security implications for many users of the RSA (algorithm)|RSA public-key cryptography|public-key authentication algorithm, as the most common key length currently in use is 1024 Binary digit|bits

Nintendo DS homebrew – SLOT-1 and SLOT-2 devices

The available systems for Game Boy Advance or Nintendo DS homebrew differ in size, compatibility with commercial ROM images, bundled special features (such as included media players), availability, and cost. To store homebrew, all flash cards use either built-in flash memory or external flash memory cards, like microSD or CompactFlash. Nintendo states the internal memory capacity of their game cartridges in binary digit|bits, while external cards state capacity in 8-bit bytes.

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