survey

Probabilistic Complex Event Recognition: A Survey

Authors:

Elias Alevizos,

Anastasios Skarlatidis,

Alexander Artikis,

Georgios PaliourasAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 50, Issue 5

Article No.: 71, Pages 1 - 31

https://doi.org/10.1145/3117809

Published: 26 September 2017 Publication History

Abstract

Complex event recognition (CER) applications exhibit various types of uncertainty, ranging from incomplete and erroneous data streams to imperfect complex event patterns. We review CER techniques that handle, to some extent, uncertainty. We examine techniques based on automata, probabilistic graphical models, and first-order logic, which are the most common ones, and approaches based on Petri nets and grammars, which are less frequently used. Several limitations are identified with respect to the employed languages, their probabilistic models, and their performance, as compared to the purely deterministic cases. Based on those limitations, we highlight promising directions for future work.

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Probabilistic Complex Event Recognition: A Survey

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cover image ACM Computing Surveys

ACM Computing Surveys Volume 50, Issue 5

September 2018

573 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3145473

Editor:
Sartaj Sahni
Department of Computer and Information Science and Engineering / University of Florida / Gainesville, FL 32611

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© 2017 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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Publication History

Published: 26 September 2017

Accepted: 01 June 2017

Revised: 01 April 2017

Received: 01 July 2016

Published in CSUR Volume 50, Issue 5

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