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    Visual Drift Detection for Sequence Data Analysis of Business Processes.

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    Author
    Yeshchenko, A; Di Ciccio, C; Mendling, J; Polyvyanyy, A
    Date
    2021-01-08
    Source Title
    IEEE Transactions on Visualization and Computer Graphics
    Publisher
    Institute of Electrical and Electronics Engineers
    University of Melbourne Author/s
    Polyvyanyy, Artem
    Affiliation
    Computing and Information Systems
    Metadata
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    Document Type
    Journal Article
    Citations
    Yeshchenko, A., Di Ciccio, C., Mendling, J. & Polyvyanyy, A. (2021). Visual Drift Detection for Sequence Data Analysis of Business Processes.. IEEE Transactions on Visualization and Computer Graphics, PP (99), pp.1-1. https://doi.org/10.1109/TVCG.2021.3050071.
    Access Status
    This item is embargoed and will be available on 2023-01-08
    URI
    http://hdl.handle.net/11343/260527
    DOI
    10.1109/TVCG.2021.3050071
    ARC Grant code
    ARC/DP180102839
    Abstract
    Event sequence data is increasingly available in various application domains, such as business process management, software engineering, or medical pathways. Processes in these domains are typically represented as process diagrams or flow charts. So far, various techniques have been developed for automatically generating such diagrams from event sequence data. An open challenge is the visual analysis of drift phenomena when processes change over time. In this paper, we address this research gap. Our contribution is a system for fine-granular process drift detection and corresponding visualizations for event logs of executed business processes. We evaluated our system both on synthetic and real-world data. On synthetic logs, we achieved an average F-score of 0.96 and outperformed all the state-of-the-art methods. On real-world logs, we identified all types of process drifts in a comprehensive manner. Finally, we conducted a user study highlighting that our visualizations are easy to use and useful as perceived by process mining experts. In this way, our work contributes to research on process mining, event sequence analysis, and visualization of temporal data.

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