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    Comprehensive Process Drift Detection with Visual Analytics

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    8
    Author
    Yeshchenko, A; Di Ciccio, C; Mendling, J; Polyvyanyy, A
    Date
    2019-10-15
    Source Title
    38th International Conference, ER 2019
    Publisher
    Springer
    University of Melbourne Author/s
    Polyvyanyy, Artem
    Affiliation
    Computing and Information Systems
    Metadata
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    Document Type
    Conference Paper
    Citations
    Yeshchenko, A., Di Ciccio, C., Mendling, J. & Polyvyanyy, A. (2019). Comprehensive Process Drift Detection with Visual Analytics. 38th International Conference, ER 2019, 11788, pp.119-135. Springer. https://doi.org/10.1007/978-3-030-33223-5_11.
    Access Status
    Open Access
    URI
    http://hdl.handle.net/11343/258888
    DOI
    10.1007/978-3-030-33223-5_11
    ARC Grant code
    ARC/DP180102839
    Abstract
    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.

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