Complex event processing: Difference between revisions

Revision as of 09:35, 22 December 2022

Revision as of 18:41, 9 April 2023

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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 Thingsthings and Smartsmart Cybercyber-physical systems==

Complex event processing is a key enabler in [[Internet of Thingsthings]] (IoT) settings and Smartsmart [[Cybercyber-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==