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Complex event recognition in the Big Data era: a survey

Giatrakos Nikolaos, Alevizos Elias, Artikis, Alexander, Deligiannakis Antonios, Garofalakis Minos

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Year 2020
Type of Item Peer-Reviewed Journal Publication
Bibliographic Citation N. Giatrakos, E. Alevizos, A. Artikis, A. Deligiannakis, and M. Garofalakis, “Complex event recognition in the Big Data era: a survey,” VLDB J., vol. 29, no. 1, pp. 313–352, Jan. 2020. doi: 10.1007/s00778-019-00557-w
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The concept of event processing is established as a generic computational paradigm in various application fields. Events report on state changes of a system and its environment. Complex event recognition (CER) refers to the identification of composite events of interest, which are collections of simple, derived events that satisfy some pattern, thereby providing the opportunity for reactive and proactive measures. Examples include the recognition of anomalies in maritime surveillance, electronic fraud, cardiac arrhythmias and epidemic spread. This survey elaborates on the whole pipeline from the time CER queries are expressed in the most prominent languages, to algorithmic toolkits for scaling-out CER to clustered and geo-distributed architectural settings. We also highlight future research directions.