Comprehensive Process Drift Detection with Visual Analytics
Web of Science
AuthorYeshchenko, A; Di Ciccio, C; Mendling, J; Polyvyanyy, A
Source Title38th International Conference, ER 2019
University of Melbourne Author/sPolyvyanyy, Artem
AffiliationComputing and Information Systems
Document TypeConference Paper
CitationsYeshchenko, A., Di Ciccio, C., Mendling, J. & Polyvyanyy, A. (2019). Comprehensive Process Drift Detection with Visual Analytics. 38th International Conference, ER 2019, 11788, pp.119-135. Springer. https://doi.org/10.1007/978-3-030-33223-5_11.
Access StatusOpen Access
ARC Grant codeARC/DP180102839
Recent research has introduced ideas from concept drift into process mining to enable the analysis of changes in business processes over time. This stream of research, however, has not yet addressed the challenges of drift categorization, drilling-down, and quantification. In this paper, we propose a novel technique for managing process drifts, called Visual Drift Detection (VDD), which fulfills these requirements. The technique starts by clustering declarative process constraints discovered from recorded logs of executed business processes based on their similarity and then applies change point detection on the identified clusters to detect drifts. VDD complements these features with detailed visualizations and explanations of drifts. Our evaluation, both on synthetic and real-world logs, demonstrates all the aforementioned capabilities of the technique.
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