Comprehensive Process Drift Detection with Visual Analytics
  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.
