TY  - CONF
TI  - Comprehensive Process Drift Detection with Visual Analytics
AB  - 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.
AF  - Conceptual Modeling - 38th International Conference, ER 2019
PP  - Salvador, Brazil
SN  - 978-3-030-33222-8
PB  - Springer
SP  - 119
EP  - 135
UR  - https://doi.org/10.1007/978-3-030-33223-5_11
PY  - 2019-01-01
AU  - Yeshchenko, Anton 
AU  - Di Ciccio, Claudio 
AU  - Mendling, Jan 
AU  - Polyvyanyy, Artem 
ER -