Dealing With Concept Drifts in Process Mining

• Published in: IEEE transaction on neural networks and learning systems Vol. 25; no. 1; pp. 154 - 171
• Main Authors: Bose, R. P. Jagadeesh Chandra, van der Aalst, Wil M. P., Zliobaite, Indre, Pechenizkiy, Mykola
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.01.2014; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Business; Concept drift; Context; Data mining; Data models; flexibility; Flowcharts; hypothesis tests; Organizations; Predictive models; process changes; Process control; process mining
• ISSN: 2162-237X; 2162-2388
• DOI: 10.1109/TNNLS.2013.2278313

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.