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Simplified process model discovery based on role-oriented genetic mining.

Weidong Zhao1, Xi Liu1, Weihui Dai2

  • 1Software School, Fudan University, No. 220 Handan Road, Shanghai 200433, China.

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This summary is machine-generated.

This study introduces a genetic programming approach for process mining, focusing on simplifying models by reducing role complexity. The method enhances process understandability and model quality, leading to more streamlined processes.

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Area of Science:

  • Business Process Management
  • Computer Science
  • Artificial Intelligence

Background:

  • Process mining techniques primarily focus on control flow, neglecting role complexity.
  • Existing role-oriented methods prioritize role correctness over model understandability.
  • High role complexity in process models hinders their quality and interpretability.

Purpose of the Study:

  • To propose a genetic programming approach for mining simplified process models.
  • To address the issue of role complexity in existing role-oriented process mining.
  • To improve the understandability and quality of process models.

Main Methods:

  • Utilizing genetic programming to mine process models from event logs.
  • Developing a novel metric for process complexity based on role cohesion and coupling.
  • Employing the new metric as a fitness function in genetic programming.

Main Results:

  • The proposed method successfully mines simpler process models by optimizing for role complexity.
  • The new role complexity metric effectively identifies and discovers roles within process models.
  • Case studies and experiments demonstrate the method's effectiveness in streamlining processes.

Conclusions:

  • Genetic programming with a role complexity metric offers an effective approach to process mining.
  • The method enhances process model understandability and quality.
  • This approach provides a valuable guideline for redesigning and optimizing business processes.