Dealing with concept drifts in process mining

R.P. Jagadeesh Chandra Bose, W.M.P. Aalst, van der, I. Zliobaite, M. Pechenizkiy

Research output: Contribution to journalArticleAcademicpeer-review

98 Citations (Scopus)
2 Downloads (Pure)

Abstract

Although most business processes change over time, contemporary process mining techniques tend to analyze these processes as if they are in a steady state. Processes may change suddenly or gradually. The drift may be periodic (e.g., because of seasonal influences) or one-of-a-kind (e.g., the effects of new legislation). For the process management, it is crucial to discover and understand such concept drifts in processes. This paper presents a generic framework and specific techniques to detect when a process changes and to localize the parts of the process that have changed. Different features are proposed to characterize relationships among activities. These features are used to discover differences between successive populations. The approach has been implemented as a plug-in of the ProM process mining framework and has been evaluated using both simulated event data exhibiting controlled concept drifts and real-life event data from a Dutch municipality.
Original languageEnglish
Pages (from-to)154-171
JournalIEEE Transactions on Neural Networks and Learning Systems
Volume25
Issue number1
DOIs
Publication statusPublished - 2014

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