Comprehensive Process Drift Detection with Visual Analytics
This work addresses the need for drift categorization, drilling-down, and quantification in process mining, which is incremental as it builds on existing concept drift ideas.
The paper tackled the problem of detecting and analyzing changes in business processes over time by introducing Visual Drift Detection (VDD), a technique that clusters declarative constraints and applies change point detection to identify drifts, with evaluation on synthetic and real-world logs demonstrating its capabilities.
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.