English

Interest-Driven Discovery of Local Process Models

Databases 2018-06-19 v1 Artificial Intelligence

Abstract

Local Process Models (LPM) describe structured fragments of process behavior occurring in the context of less structured business processes. Traditional LPM discovery aims to generate a collection of process models that describe highly frequent behavior, but these models do not always provide useful answers for questions posed by process analysts aiming at business process improvement. We propose a framework for goal-driven LPM discovery, based on utility functions and constraints. We describe four scopes on which these utility functions and constrains can be defined, and show that utility functions and constraints on different scopes can be combined to form composite utility functions/constraints. Finally, we demonstrate the applicability of our approach by presenting several actionable business insights discovered with LPM discovery on two real life data sets.

Keywords

Cite

@article{arxiv.1703.07116,
  title  = {Interest-Driven Discovery of Local Process Models},
  author = {Niek Tax and Benjamin Dalmas and Natalia Sidorova and Wil M P van der Aalst and Sylvie Norre},
  journal= {arXiv preprint arXiv:1703.07116},
  year   = {2018}
}

Comments

submitted to the International Conference on Business Process Management (BPM) 2017

R2 v1 2026-06-22T18:52:11.760Z