CEP: Complex Event Processing
What is CEP or Complex Event Processing?
Complex Event Processing (CEP) is a technology for low-latency filtering, correlating, aggregating, and computing on real- world event data.
The academic origins of CEP began with research efforts at Cal Tech (Mani Chandy) and Stanford University (David Luckham) in the mid-1990s, with a focus on processing streaming event data by identifying complex sequences of events within some specified time interval, and then triggering an appropriate action or alert as a result of these real-time analytics. In 2002, the publication of Luckham's book, "The Power of Events: An Introduction to Complex Event Processing in Distributed Enterprise Systems" helped popularize the industry term “complex event processing” or CEP. Additional research on a data-centric approach to the CEP problem was undertaken at MIT, Brown, and Brandeis Universities (Aurora Project led by Mike Stonebraker, Stan Zdonik, Hari Balakrishan, and Mitch Cherniak) and at Stanford (STREAM Project led by Jennifer Widom).
In discussing CEP, it is critical to define several terms. The processing of messages as they arrive is called, "real-time processing", and the use of a sophisticated and optimized storage mechanism is called "historical processing". Another term that shows up in the CEP literature is an "event cloud" or "cloud" for short. A "cloud" can really be thought of as a manifestation of historical processing coupled with real-time processing. The power of a good CEP engine is in how well it integrates real-time and historical processing. In other words, a cloud can be easily simulated by a modern CEP system.
Additional descriptions of CEP concepts, principles, and practices can be found below.
Streaming SQL and CEP
CEP Clouds
CEP Causality
CEP Pattern matching
Example: CEP and Causality
CEP Glossary
Links to additional academic and industry information about CEP can be found at:
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