Digital Library of the European Council for Modelling and Simulation

Title:	Concept Drift Detection Of Event Streams Using An Adaptive Window
Authors:	Marwan Hassani
Published in:	(2019). ECMS 2019 Proceedings Edited by: Mauro Iacono, Francesco Palmieri, Marco Gribaudo, Massimo Ficco, European Council for Modeling and Simulation. DOI: http://doi.org/10.7148/2019 ISSN: 2522-2422 (ONLINE) ISSN: 2522-2414 (PRINT) ISSN: 2522-2430 (CD-ROM) 33rd International ECMS Conference on Modelling and Simulation, Caserta, Italy, June 11^th – June 14^th, 2019
Citation format:	Marwan Hassani (2019). Concept Drift Detection Of Event Streams Using An Adaptive Window, ECMS 2019 Proceedings Edited by: Mauro Iacono, Francesco Palmieri, Marco Gribaudo, Massimo Ficco European Council for Modeling and Simulation. doi: 10.7148/2019-0230
DOI:	https://doi.org/10.7148/2019-0230
Abstract:	Process mining is an emerging data mining task of gathering valuable knowledge out of the huge collec-tions of business operation data. Despite its relatively young age, it has successfully provided many new in-sights into business workflows using established data mining techniques. Recently, with the huge improve-ments in the technologies of sensoring, collection and storing of data, a big demand for both shorter mining times and adaptive models of streaming process events arose. This initiated the field of stream process mining very recently. Drifts in the underlying concepts of the business processes are of a great interest for decision makers. One important advantage of stream process mining techniques over static ones is the ability to de-tect such drifts and to adapt its models accordingly. In this paper, we introduce an efficient approach that uses the collected information of an event stream miner to detect concept drifts. We use a dynamic window, which grows in size for stationary process behavior and shrinks for diverting data and thus indicating a concept drift. This adaptive window is used to build a model by focusing only on up-to-date information and dis-carding outdated items. Extensive experimental eval-uations over real and synthetic log files show the abil-ity of our algorithm to detect sudden drifts. We addi-tionally show the effectiveness of our concept detection method in setting the pruning period of a recent stream mining algorithm.
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