Comprehensive Process Drift Detection with Visual Analytics

  • Anton YeshchenkoEmail author
  • Claudio Di Ciccio
  • Jan Mendling
  • Artem Polyvyanyy
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11788)


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.


Process mining Process drifts Declarative process models 



This work is partially funded by the EU H2020 program under MSCA-RISE agreement 645751 (RISE_BPM). Artem Polyvyanyy was partly supported by the Australian Research Council Discovery Project DP180102839.


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Vienna University of Economics and BusinessViennaAustria
  2. 2.The University of MelbourneParkvilleAustralia

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