Facilitating the Quantitative Analysis of Complex Events through a Computational Intelligence Model-Driven Tool

Complex event processing (CEP) is a computational intelligence technology capable of analyzing big data streams for event pattern recognition in real time. In particular, this technology is vastly useful for analyzing multicriteria conditions in a pattern, which will trigger alerts (complex events)...

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Published in:Scientific programming 2019, Vol.2019 (2019), p.1-17
Main Authors: Boubeta-Puig, Juan, Valero, Valentín, Macià, Hermenegilda, Díaz, Gregorio, Ortiz, Guadalupe
Format: Article
Language:English
Subjects:
Artificial intelligence
Big Data
Computer simulation
Data transmission
Engineering
Intelligence
International conferences
Multiple criterion
Pattern recognition
Petri nets
Programming languages
Quantitative analysis
Semantics
Software
Subject specialists
Technology assessment
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cited_by cdi_FETCH-LOGICAL-c360t-518cf901efe14211ad534239767e152e598b7dd547155e4bc327b81197eb0fc23
cites cdi_FETCH-LOGICAL-c360t-518cf901efe14211ad534239767e152e598b7dd547155e4bc327b81197eb0fc23
container_end_page 17
container_issue 2019
container_start_page 1
container_title Scientific programming
container_volume 2019
creator Boubeta-Puig, Juan
Valero, Valentín
Macià, Hermenegilda
Díaz, Gregorio
Ortiz, Guadalupe
description Complex event processing (CEP) is a computational intelligence technology capable of analyzing big data streams for event pattern recognition in real time. In particular, this technology is vastly useful for analyzing multicriteria conditions in a pattern, which will trigger alerts (complex events) upon their fulfillment. However, one of the main challenges to be faced by CEP is how to define the quantitative analysis to be performed in response to the produced complex events. In this paper, we propose the use of the MEdit4CEP-CPN model-driven tool as a solution for conducting such quantitative analysis of events of interest for an application domain, without requiring knowledge of any scientific programming language for implementing the pattern conditions. Precisely, MEdit4CEP-CPN facilitates domain experts to graphically model event patterns, transform them into a Prioritized Colored Petri Net (PCPN) model, modify its initial marking depending on the application scenario, and make the quantitative analysis through the simulation and monitor capabilities provided by CPN tools.
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subjects Artificial intelligence
Big Data
Computer simulation
Data transmission
Engineering
Intelligence
International conferences
Multiple criterion
Pattern recognition
Petri nets
Programming languages
Quantitative analysis
Semantics
Software
Subject specialists
Technology assessment
title Facilitating the Quantitative Analysis of Complex Events through a Computational Intelligence Model-Driven Tool
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