Comprehensive Process Drift Detection with Visual Analytics

Anton Yeshchenko, Claudio Di Ciccio, Jan Mendling, Artem Polyvyanyy

Publication: Chapter in book/Conference proceedingContribution to conference proceedings


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.
Original languageEnglish
Title of host publicationConceptual Modeling - 38th International Conference, ER 2019
Editors Alberto H.F. Laender, Barbara Pernici, Ee-Peng Lim, José Palazzo M. de Oliveira
Place of PublicationSalvador, Brazil
Pages119 - 135
ISBN (Print)978-3-030-33222-8
Publication statusPublished - 2019

Austrian Classification of Fields of Science and Technology (ÖFOS)

  • 102022 Software development
  • 102
  • 102001 Artificial intelligence
  • 102013 Human-computer interaction
  • 502
  • 502050 Business informatics

    Di Ciccio, C. (Researcher) & Mendling, J. (Researcher)


    Project: Research

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