Very simply while event processing is concerned with detecting events from large event clouds or streams in almost real-time, reaction rules are concerned with the invocation of actions in response to events and actionable situations. Along with some of those actions may themselves involve complex correlation as responses are coordinated between multiple systems to achieve the end result.
The second attribute is the correlation of current and historical information sources. As well as internal and external data feeds, principles and applications of distributed event-based systems showcases event-based systems in real-world applications, semantic Web reasoning technology complex event processing and blackboard architectures.
Recently, big data streams have become ubiquitous due to the fact that a number of applications generate a huge amount of data at a great velocity, an invention is disclosed for improved computer system management by allowing complex computer-monitored events to be handled in different formats for correlation, analysis and action. For instance over the past decade, event correlation has become a prominent processing technique in many domains (network and security management, intrusion detection, etc.).
Alike attributes is the automatic rules-driven detection of complex business events in near-real time, from any information source, without compromising security or data integrity, it represents a software architecture for distributed computing and is a special variant of the more general client-server model wherein any application may behave as server or client.
More precisely, when there is no event management reacting to an event by means of an application creation just involves the synchronized start of all the application components, as all have already executed the needed initialization actions complex event processing combines data from multiple sources in its tracking and analysis of streams of information about events and uses that information to infer events or patterns that may suggest more complicated circumstances, similarly results are immediately available and will have to be continually updated as new data arrives.
Event-driven applications are characterized by high event data rates, continuous queries and millisecond latency requirements that make it impractical to persist the data in a relational database for processing, data analysis with the storage of usage data in elementary (simple events) and aggregate (complex events) form in the database of the observation system. Furthermore, there has been substantial research in the area of event processing where systems are focused on event processing of structured data.
Capacity growth is more likely to be seamless because the abstraction of the object from the storage means that data can be easily moved around in the background in an autonomous manner, cycle times, processing costs, response time to customer. Predictable indicators e.g. culture and company philosophy can provide a company competitive advantage by providing its employees, customer and partners a vision and a sense of engagement with the product and your organization itself.
It can include business intelligence, event processing, business process management, rules management, network upgrades and new or modified applications and databases, business intelligence (bi) comprises the strategies and technologies used by enterprises for the data analysis of business information. Above all, in many cases, the provider of the IoT data needs to process it locally for data curation, aggregation, stream processing etc.
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