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2018 | OriginalPaper | Chapter

Change Detection in Event Logs by Clustering

Authors : Agnes Koschmider, Daniel Siqueira Vidal Moreira

Published in: On the Move to Meaningful Internet Systems. OTM 2018 Conferences

Publisher: Springer International Publishing

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Abstract

The detection of changes in event logs recording the behavior of flexible processes is especially challenging and process mining algorithms generate useless “spaghetti” models out of them. Due to this, existing approaches for change detection in event logs recording the behavior of flexible processes can only localize a change point, which is of no avail when it comes to explain when, why and how a process model changed and will change. The aim of this paper is to present a novel clustering technique laying the foundation to determine a variety of changes and to foresee changes. In order to do this, four algorithms have been developed. We report the results of evaluations on synthetic as well as real-life data demonstrating the efficiency of the approach and also its broad scope of application for event and sensor data.

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Title: Change Detection in Event Logs by Clustering
Authors: Agnes Koschmider
Daniel Siqueira Vidal Moreira
Publisher: Springer International Publishing
Book: On the Move to Meaningful Internet Systems. OTM 2018 Conferences
Print ISBN: 978-3-030-02609-7

Electronic ISBN: 978-3-030-02610-3

Copyright Year: 2018
DOI: https://doi.org/10.1007/978-3-030-02610-3_36

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