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

Abstract

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.

A Compositional Approach for Complex Event Pattern Modeling and Transformation to Colored Petri Nets with Black Sequencing Transitions

“…All of these test cases can be found at this URL 8 , where the reader can find the graphical models created with MEdit4CEP-BPCPN, and the BPCPNs and PCPNs obtained for them with the test scenarios. As an illustration, we describe Test cases 1, 2 and 11, with the input 8. http://dx.doi.org/10.17632/scrjyndpv6.1 (1,15,1,pA({ma=23,tm=1}))++ 1` (2,15,1,pA({ma=55,tm=1}))++ 1` (3,17,1,pC({mc=0,tm=1}))++ 1`(4,16,2,pB({mb=25,tm=2}))++ 1`(5,15,2,pA({ma=50,tm=2}))++ 1`(6,17,3,pC({mc=5,tm=3}))++ 1`(7,16,3,pB({mb=12,tm=3}))++ 1`(8,17,4,pC({mc=10,tm=4}))++ 1`(9,15,4,pA({ma=0,tm=4}))++ 1`(10,16,5,pB({mb=1,tm=5}))++ 1` (11,16,6,pB({mb=5,tm=6}))++ 1`(12,15,7,pA({ma=15,tm=7}))++ 1`(13,17,7,pC({mc=8,tm=7})) Fig. 17.…”

“…We also developed MEdit4CEP-CPN [10], an extension of MEdit4CEP that allows the user to define event patterns using the MEdit4CEP graphical editor, and then automatically transform them into PCPNs, thus obtaining the input files that can be immediately opened with CPN Tools. MEdit4CEP-CPN was then used in [11] as a computational intelligence modeldriven tool for the quantitative analysis of events of interest in the context of a specific application, the Sick Building Syndrome.…”