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.
|Title of host publication||Perspectives in Business Informatics Research |
|Subtitle of host publication||14th International Conference, BIR 2015, Tartu, Estonia, August 26-28, 2015, Proceedings|
|Editors||R. Matulevicius, M. Dumas|
|Place of Publication||Dordrecht|
|Publication status||Published - 2015|
|Name||Lecture Notes in Business Information Processing|