Change point detection and dealing with gradual and multi-order dynamics in process mining
Conference ContributionMartjushev, J., Jagadeesh Chandra Bose, R.P. & van der Aalst, W.M.P. (2015). Change point detection and dealing with gradual and multi-order dynamics in process mining. In R. Matulevicius & M. Dumas (Eds.), Perspectives in Business Informatics Research (pp. 161-178). (Lecture Notes in Business Information Processing, No. 229). Dordrecht: Springer. In Scopus Cited 7 times.
In recent years process mining techniques have matured. Provided that the process is stable and enough example traces have been recorded in the event log, it is possible to discover a high-quality process model that can be used for performance analysis, compliance checking, and prediction. Unfortunately, most processes are not in steady-state and process discovery techniques have problems uncovering "second-order dynamics" (i.e., the process itself changes while being analyzed). This paper describes an approach to discover a variety of concept drifts in processes. Unlike earlier approaches, we can discover gradual drifts and multi-order dynamics (e.g., recurring seasonal effects mixed with the effects of an economic crisis). We use a novel adaptive windowing approach to robustly localize changes (gradual or sudden). Our extensive evaluation (based on objective criteria) shows that the new approach is able to efficiently uncover a broad range of drifts in processes.