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Related Experiment Videos

Visual Drift Detection for Event Sequence Data of Business Processes.

Anton Yeshchenko, Claudio Di Ciccio, Jan Mendling

    IEEE Transactions on Visualization and Computer Graphics
    |January 8, 2021
    PubMed
    Summary

    This study introduces a novel system for detecting and visualizing process changes over time in event sequence data. The system accurately identifies process drifts and offers user-friendly visualizations for process mining experts.

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    Area of Science:

    • Process mining
    • Event sequence analysis
    • Temporal data visualization

    Background:

    • Event sequence data is prevalent across domains like business process management and healthcare.
    • Existing methods for generating process diagrams from event data do not adequately address the visual analysis of process changes over time.
    • Detecting and visualizing process drift remains an open challenge in the field.

    Purpose of the Study:

    • To address the research gap in visual analysis of process drift phenomena.
    • To develop a system for fine-granular process drift detection and visualization.
    • To evaluate the system's effectiveness on synthetic and real-world event logs.

    Main Methods:

    • Developed a system for fine-granular process drift detection.

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  • Implemented corresponding visualizations for event logs of executed business processes.
  • Evaluated the system using synthetic and real-world datasets.

    • Main Results:

    • Achieved an average F-score of 0.96 on synthetic logs, outperforming state-of-the-art methods.
    • Successfully identified all types of process drifts comprehensively on real-world logs.
    • User study confirmed visualizations are easy to use and useful for process mining experts.

    Conclusions:

    • The developed system effectively detects and visualizes process drifts in event logs.
    • The system demonstrates superior performance compared to existing methods.
    • The visualizations enhance the understanding of temporal process changes for domain experts.