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Dealing with concept drifts in process mining.

R P Jagadeesh Chandra Bose, Wil M P van der Aalst, Indre Zliobaite

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    Summary
    This summary is machine-generated.

    This study introduces a framework to detect concept drift in business processes, identifying changes over time. It helps manage processes by pinpointing evolving activities for better understanding and adaptation.

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    Area of Science:

    • Business Process Management
    • Data Mining
    • Software Engineering

    Background:

    • Contemporary process mining assumes steady-state processes, neglecting temporal changes.
    • Business processes are dynamic, experiencing gradual or sudden concept drifts.
    • Understanding process evolution is critical for effective process management.

    Purpose of the Study:

    • To develop a framework for detecting and localizing concept drifts in business processes.
    • To provide techniques for identifying when and where process changes occur.
    • To enhance process management by enabling the analysis of dynamic processes.

    Main Methods:

    • Proposed a generic framework with specific techniques for concept drift detection.
    • Utilized features to characterize activity relationships and identify differences between process populations.
    • Implemented the approach as a plug-in for the ProM process mining framework.

    Main Results:

    • Successfully detected and localized concept drifts in simulated and real-life event data.
    • Demonstrated the framework's ability to identify changes in process behavior.
    • Validated the effectiveness of the proposed features in characterizing process evolution.

    Conclusions:

    • The developed framework effectively detects and localizes concept drifts in business processes.
    • The techniques provide valuable insights into process dynamics, aiding process management.
    • The ProM plug-in offers a practical tool for analyzing evolving business processes.