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PGraphD*: Methods for Drift Detection and Localisation Using Deep Learning Modelling of Business Processes.

Khadijah Muzzammil Hanga1, Yevgeniya Kovalchuk2, Mohamed Medhat Gaber1,3

  • 1School of Computing and Digital Technology, Birmingham City University, Birmingham B4 7XG, UK.

Entropy (Basel, Switzerland)
|July 27, 2022
PubMed
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This summary is machine-generated.

This study introduces PGraphD* methods for business process drift detection and localization. PGraphDD-SS offers superior accuracy and faster detection compared to existing methods.

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Data Mining

Background:

  • Business process monitoring is crucial for operational efficiency.
  • Detecting and localizing process drifts are key challenges in real-world applications.
  • Existing drift detection methods often lack accuracy or speed.

Purpose of the Study:

  • To introduce PGraphD*, a novel framework for business process drift detection and localization.
  • To present two new drift detection methods: PGraphDD-QM and PGraphDD-SS.
  • To introduce PGraphDL for drift localization.

Main Methods:

  • PGraphD* methods leverage deep learning and graph-based approaches.
  • PGraphDD-QM utilizes a quality metric for drift detection.
  • PGraphDD-SS employs a similarity score for enhanced drift detection.
Keywords:
business process managementconcept drift detectionconcept drift localisationdeep learninggraph streamslong short-term memoryprocess mining

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Main Results:

  • PGraphDD-SS achieved 100% accuracy on most synthetic logs and 80% on a complex real-life log.
  • PGraphDD-SS demonstrated a 59% reduction in average drift detection delay compared to state-of-the-art methods.
  • PGraphDD-SS outperformed PGraphDD-QM in drift detection accuracy.

Conclusions:

  • PGraphD* framework provides effective solutions for business process drift.
  • PGraphDD-SS is a highly accurate and efficient method for drift detection.
  • The developed methods advance the field of process mining and business process management.