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RandFreeman (talk | contribs) Adding local short description: "Method of analysing information about events", overriding Wikidata description "approach to processing events in software engineering, aiming to identify meaningful events such as opportunities or threats in real-time situations and respond as quickly as possible" |
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{{Short description|Method of analysing information about events}}
'''Event processing''' is a method of tracking and [[data analytics|analyzing]] (processing) streams of information (data) about things that happen (events),<ref name=LuckhamD>{{cite book|last=Luckham|first=David C.|title=Event Processing for Business: Organizing the Real-Time Enterprise|url=http://ee.stanford.edu/~luckham/|publisher=John Wiley & Sons, Inc.
These events may be happening across the various layers of an organization as sales leads, orders or [[customer service]] calls. Or, they may be news items,<ref>{{citation|last=Crosman|first=Penny|title=Aleri, Ravenpack to Feed News into Trading Algos|url=http://www.wallstreetandtech.com/articles/217500395|publisher=Wall Street & Technology|date=May 18, 2009}}{{Dead link|date=July 2019 |bot=InternetArchiveBot |fix-attempted=yes }}</ref> [[text
Analysts The vast amount of information available about events is sometimes referred to as the event cloud.<ref name=LuckhamD />
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* Event-[[pattern detection]]
* Event [[Abstraction (computer science)|abstraction]]
* Event filtering
* Event aggregation and transformation
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* Abstracting [[event-driven programming|event-driven]] processes
Commercial applications of CEP exist in variety of industries and include
[http://www.complexevents.com Details of commercial products and use cases]</ref>
==History==
The CEP area has roots in [[discrete event simulation]], the [[active database]] area and some programming languages. The activity in the industry was preceded by a wave of research projects in the 1990s. According to
==Related concepts==
CEP is used in [[operational intelligence]] (OI) products to provide insight into business operations by running query analysis against live feeds and event data. OI collects real-time data and
In [[network management]], [[systems management]], [[application management]] and [[service management]], people usually refer instead to [[event correlation]]. As CEP engines, event correlation engines (''event correlators'') analyze a mass of events, pinpoint the most significant ones, and trigger actions. However, most of them do not produce new inferred events. Instead, they relate high-level events with low-level events.<ref>J.P. Martin-Flatin, G. Jakobson and L. Lewis, "Event Correlation in Integrated Management: Lessons Learned and Outlook", Journal of Network and Systems Management, Vol. 17, No. 4, December 2007.</ref>
[[Inference engine]]s, e.g., [[rule-based system|rule-based reasoning engine]]s, typically produce inferred information in [[artificial intelligence]]. However, they do not usually produce new information in the form of complex (i.e., inferred) events.
==Example==
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Computation-oriented CEP's role can arguably be seen to overlap with Business Rule technology.
For example, customer service centers are using CEP for click-stream analysis and customer experience management. CEP software can factor real-time information about millions of events (clicks or other interactions) per second into [[business intelligence]] and other [[decision-support]] applications. These "[[recommendation
==Integration with time series databases==
A [[time series database]] is a software system that is optimized for the handling of data organized by time. Time series are finite or infinite sequences of data items, where each item has an associated timestamp and the sequence of timestamps is non-decreasing. Elements of a time series are often called ticks. The timestamps are not required to be ascending (merely non-decreasing) because in practice the time resolution of some systems such as financial data sources can be quite low (milliseconds, microseconds or even nanoseconds), so consecutive events may carry equal timestamps.
Time series data provides a historical context to the analysis typically associated with complex event processing. This can apply to any vertical industry such as finance<ref>
The ideal case for CEP analysis is to view historical time series and real-time streaming data as a single time continuum. What happened yesterday, last week or last month is simply an extension of what is occurring today and what may occur in the future. An example may involve comparing current market volumes to historic volumes, prices and volatility for trade execution logic. Or the need to act upon live market prices may involve comparisons to benchmarks that include sector and index movements, whose intra-day and historic trends gauge volatility and smooth outliers.▼
==Internet of things and smart cyber-physical systems==
▲The ideal case for CEP analysis is to view historical time series and real-time streaming data as a single time continuum. What happened yesterday, last week or last month is simply an extension of what is occurring today and what may occur in the future. An example may involve comparing current market volumes to historic volumes, prices and volatility for trade execution logic. Or the need to act upon live market prices may involve comparisons to benchmarks that include sector and index movements, whose intra-day and historic trends gauge volatility and smooth outliers.
Complex event processing is a key enabler in [[Internet of things]] (IoT) settings and smart [[cyber-physical system]]s (CPS) as well. Processing dense and heterogeneous streams from various sensors and matching patterns against those streams is a typical task in such cases.<ref>{{cite web|url=http://msdl.cs.mcgill.ca/people/istvan/pub/mtcps2016|title=Balogh, Dávid, Ráth, Varró, Vörös: Distributed and Heterogeneous Event-based Monitoring in Smart Cyber-Physical Systems, In 1st Workshop on Monitoring and Testing of Cyber-Physical Systems, Vienna, Austria. 2016.}}</ref> The majority of these techniques rely on the fact that representing the IoT system's state and its changes is more efficient in the form of a data stream, instead of having a static, materialized model. Reasoning over such stream-based models fundamentally differs from traditional reasoning techniques and typically require the combination of [[model transformation]]s and CEP.<ref>I. Dávid, I. Ráth, D. Varró: Foundations for Streaming Model Transformations by Complex Event Processing, International Journal on Software and Systems Modeling, pp 1--28, 2016. {{doi|10.1007/s10270-016-0533-1}}</ref>
==See also==
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===Vendors and products===
*
* [[Azure Stream Analytics]]
* [[Drools|Drools Fusion]]
* [[Esper (software)|Esper]] Complex event processing for Java and C# (GPLv2).
* [[Feedzai|Feedzai - Pulse]]
* [[Microsoft|Microsoft StreamInsight]] Microsoft CEP Engine implementation
▲* [[Microsoft|Microsoft StreamInsight]] Microsoft CEP Engine implementation <ref>[http://technet.microsoft.com/en-us/library/ee362541(v=sql.111).aspx Microsoft StreamInsight product page]</ref>
* [[openPDC]] — A set of applications for processing streaming time-series data in real-time.
* [[Oracle SOA Suite|Oracle Event Processing]] - for building applications to filter, correlate, and process events in real time.
* [[SAP SE|SAP ESP]] - A low-latency, rapid development and deployment platform that allows processing multiple streams of data in real time
*
▲* [[SAP SE|SAP ESP]] - A low-latency, rapid development and deployment platform that allows processing multiple streams of data in real time <ref>[http://scn.sap.com/community/developer-center/esp SAP ESP - Developers community]</ref>
▲* [[Sqlstream|SQLstream]] SQLstream’s stream processing platform, s-Server, provides a relational stream computing platform for analyzing large volumes of service, sensor and machine and log file data in real-time.
* [[TIBCO| TIBCO BusinessEvents & Streambase ]] - CEP platform and High Performance Low Latency Event Stream Processing
* [[WebSphere Business Events]]
* [[Apache Flink]] Open-source distributed stream processing framework with a CEP API<ref>{{cite web|url=https://ci.apache.org/projects/flink/flink-docs-release-1.2/dev/libs/cep.html|title=Apache Flink 1.2 Documentation: FlinkCEP - Complex event processing for Flink|website=ci.apache.org}}</ref> for Java and Scala.
* [[Apache
==References==
{{reflist}}
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