Complex event processing
Event processing is a method of tracking and
These events may be happening across the various layers of an organization as sales leads, orders or
Analysts have suggested that CEP will give organizations a new way to analyze patterns in real-time and help the business side communicate better with IT and service departments.
The vast amount of information available about events is sometimes referred to as the event cloud.[1]
Conceptual description
Among thousands of incoming events, a monitoring system may for instance receive the following three from the same source:
- church bells ringing.
- the appearance of a man in a tuxedo with a woman in a flowing white gown.
- rice flying through the air.
From these events the monitoring system may infer a complex event: a wedding. CEP as a technique helps discover complex events by analyzing and correlating other events:[5] the bells, the man and woman in wedding attire and the rice flying through the air.
CEP relies on a number of techniques,[6] including:
- Event-pattern detection
- Event abstraction
- Event filtering
- Event aggregation and transformation
- Modeling event hierarchies
- Detecting relationships (such as membership or timing) between events
- Abstracting event-driven processes
Commercial applications of CEP exist in variety of industries and include the detection of credit-card fraud, business activity monitoring, and security monitoring.[7]
History
The CEP area has roots in
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 correlates against historical data to provide insight and analysis. Multiple sources of data can be combined to provide a common operating picture that uses current information.
In
Inference engines, e.g., rule-based reasoning engines, 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
A more systemic example of CEP involves a car, some sensors and various events and reactions. Imagine that a car has several sensors—one that measures tire pressure, one that measures speed, and one that detects if someone sits on a seat or leaves a seat.
In the first situation, the car is moving and the pressure of one of the tires moves from 45 psi to 41 psi over 15 minutes. As the pressure in the tire is decreasing, a series of events containing the tire pressure is generated. In addition, a series of events containing the speed of the car is generated. The car's Event Processor may detect a situation whereby a loss of tire pressure over a relatively long period of time results in the creation of the "lossOfTirePressure" event. This new event may trigger a reaction process to note the pressure loss into the car's maintenance log, and alert the driver via the car's portal that the tire pressure has reduced.
In the second situation, the car is moving and the pressure of one of the tires drops from 45 psi to 20 psi in 5 seconds. A different situation is detected—perhaps because the loss of pressure occurred over a shorter period of time, or perhaps because the difference in values between each event were larger than a predefined limit. The different situation results in a new event "blowOutTire" being generated. This new event triggers a different reaction process to immediately alert the driver and to initiate onboard computer routines to assist the driver in bringing the car to a stop without losing control through skidding.
In addition, events that represent detected situations can also be combined with other events in order to detect more complex situations. For example, in the final situation the car is moving normally and suffers a blown tire which results in the car leaving the road and striking a tree, and the driver is thrown from the car. A series of different situations are rapidly detected. The combination of "blowOutTire", "zeroSpeed" and "driverLeftSeat" within a very short period of time results in a new situation being detected: "occupantThrownAccident". Even though there is no direct measurement that can determine conclusively that the driver was thrown, or that there was an accident, the combination of events allows the situation to be detected and a new event to be created to signify the detected situation. This is the essence of a complex (or composite) event. It is complex because one cannot directly detect the situation; one has to infer or deduce that the situation has occurred from a combination of other events.
Integration with business process management
A natural fit for CEP has been with business process management (BPM).[11] BPM focuses on end-to-end business processes, in order to continuously optimize and align for its operational environment.
However, the optimization of a business does not rely solely upon its individual, end-to-end processes. Seemingly disparate processes can affect each other significantly. Consider this scenario: In the aerospace industry, it is good practice to monitor breakdowns of vehicles to look for trends (determine potential weaknesses in manufacturing processes, material, etc.). Another separate process monitors current operational vehicles' life cycles and decommissions them when appropriate. One use for CEP is to link these separate processes, so that in the case of the initial process (breakdown monitoring) discovering a malfunction based on metal fatigue (a significant event), an action can be created to exploit the second process (life cycle) to issue a recall on vehicles using the same batch of metal discovered as faulty in the initial process.
The integration of CEP and BPM must exist at two levels, both at the business awareness level (users must understand the potential holistic benefits of their individual processes) and also at the technological level (there needs to be a method by which CEP can interact with BPM implementation). For a recent state of the art review on the integration of CEP with BPM, which is frequently labeled as Event-Driven Business Process Management, refer to.[12]
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
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[14] and cooperatively with other technologies such as BPM.
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
Complex event processing is a key enabler in
See also
- Event correlation
- Event-driven architecture — (EDA) is a software architecture pattern promoting the production, detection, consumption of, and reaction to events.
- SEDA - Staged event-driven architecture decomposes complex, event-driven architectures into stages
- Event Processing Technical Society — (EPTS) is an event processing community of interest
- Event stream processing— (ESP) is a related technology that focuses on processing streams of related data.
- Kinetic Rule Language — (KRL) is an event-condition-action rule language with an embedded complex event expression language.
- Operational intelligence — Both CEP and ESP are technologies that underpin operational intelligence.
- Pattern matching
- Real-time business intelligence — Business Intelligence is the application of knowledge derived from CEP systems
- Real-time computing — CEP systems are typically real-time systems
- Real time enterprise
Vendors and products
- Apama by Software AG - monitors rapidly moving event streams, detects and analyzes important patterns, and takes action according to rules.[17]
- Azure Stream Analytics
- Drools Fusion
- EVAM Streaming Analytics
- Esper Complex event processing for Java and C# (GPLv2).
- Feedzai - Pulse
- Microsoft StreamInsight Microsoft CEP Engine implementation[18]
- openPDC — A set of applications for processing streaming time-series data in real-time.
- Oracle Event Processing - for building applications to filter, correlate, and process events in real time.
- SAP ESP - A low-latency, rapid development and deployment platform that allows processing multiple streams of data in real time[19]
- 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 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[20] for Java and Scala.
- Apache Storm Free and open source distributed realtime computation system. Storm processes unbounded streams of data in realtime.
References
- ^ ISBN 978-0-470-53485-4.
- ^ Bates, John (15 June 2011), John Bates of Progress explains how complex event processing works and how it can simplify the use of algorithms for finding and capturing trading opportunities, Fix Global Trading, retrieved May 14, 2012
- ^ Crosman, Penny (May 18, 2009), Aleri, Ravenpack to Feed News into Trading Algos, Wall Street & Technology[permanent dead link]
- ^ McKay, Lauren (August 13, 2009), Forrester Gives a Welcoming Wave to Complex Event Processing, Destination CRM
- ^ D. Luckham, "The Power of Events: An Introduction to Complex Event Processing in Distributed Enterprise Systems", Addison-Wesley, 2002.
- ^ O. Etzion and P. Niblett, "Event Processing in Action", Manning Publications, 2010.
- ^ Details of commercial products and use cases
- ^ Leavit, Neal (April 2009), Complex-Event Processing Poised for Growth, Computer, vol. 42, no. 4, pp. 17-20 Washington
- – via Dagstuhl Research Online Publication Server.
- ^ 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.
- ^ Kobielus, James (September 2008), Really Happy in Real Time, Destination CRM
- ^ "Time Series in Finance". cs.nyu.edu.
- ^ "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".
- ^ Apama Real-Time Analytics Overview Archived 2015-10-25 at the Wayback Machine. Softwareag.com. Retrieved on 2013-09-18.
- ^ "Microsoft StreamInsight". technet.microsoft.com. 28 July 2016.
- ^ "SAP ESP - Developers community". Archived from the original on 2015-01-05. Retrieved 2014-07-17.
- ^ "Apache Flink 1.2 Documentation: FlinkCEP - Complex event processing for Flink". ci.apache.org.