VDD: A visual drift detection system for process mining

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    Author
    Yeshchenko, A; Mendling, J; Di Ciccio, C; Polyvyanyy, A
    Date
    2020-01-01
    Source Title
    CEUR Workshop Proceedings
    Publisher
    CEUR Workshop Proceedings
    University of Melbourne Author/s
    Polyvyanyy, Artem
    Affiliation
    Computing and Information Systems
    Metadata
    Show full item record
    Document Type
    Conference Paper
    Citations
    Yeshchenko, A., Mendling, J., Di Ciccio, C. & Polyvyanyy, A. (2020). VDD: A visual drift detection system for process mining. Proceedings of the ICPM Doctoral Consortium and Tool Demonstration Track 2020 co-located with the 2nd International Conference on Process Mining (ICPM 2020), 2703, pp.31-34. CEUR Workshop Proceedings.
    Access Status
    Open Access
    URI
    http://hdl.handle.net/11343/258905
    Open Access URL
    http://ceur-ws.org/Vol-2703/paperTD4.pdf
    ARC Grant code
    ARC/DP180102839
    Abstract
    Research on concept drift detection has inspired recent advancements of process mining and expanding the growing arsenal of process analysis tools. What has so far been missing in this new research stream are techniques that support comprehensive process drift analysis in terms of localizing, drillingdown, quantifying, and visualizing process drifts. In our research, we built on ideas from concept drift, process mining, and visualization research and present a novel web-based software tool to analyze process drifts, called Visual Drift Detection (VDD). Addressing the comprehensive analysis requirements, our tool is of benefit to researchers and practitioners in the business intelligence and process analytics area. It constitutes a valuable aid to those who are involved in business process redesign projects.

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