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Data Mining and Knowledge Discovery

, Volume 13, Issue 1, pp 67–87 | Cite as

A Rule-Based Approach for Process Discovery: Dealing with Noise and Imbalance in Process Logs

  • Laura Măruşter
  • A. J. M. M. (TON) Weijters
  • Wil M. P. Van Der Aalst
  • Antal Van Den Bosch
Article

Abstract

Effective information systems require the existence of explicit process models. A completely specified process design needs to be developed in order to enact a given business process. This development is time consuming and often subjective and incomplete. We propose a method that constructs the process model from process log data, by determining the relations between process tasks. To predict these relations, we employ machine learning technique to induce rule sets. These rule sets are induced from simulated process log data generated by varying process characteristics such as noise and log size. Tests reveal that the induced rule sets have a high predictive accuracy on new data. The effects of noise and imbalance of execution priorities during the discovery of the relations between process tasks are also discussed. Knowing the causal, exclusive, and parallel relations, a process model expressed in the Petri net formalism can be built. We illustrate our approach with real world data in a case study.

Keywords

rule induction process mining knowledge discovery Petri nets 

Notes

Acknowledgments

We would like to thank Dr. Christine Pelletier (University of Groningen) for her valuable comments and remarks during the review of our paper.

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Copyright information

© Springer Science + Business Media, LLC 2006

Authors and Affiliations

  • Laura Măruşter
    • 1
  • A. J. M. M. (TON) Weijters
    • 2
  • Wil M. P. Van Der Aalst
    • 2
  • Antal Van Den Bosch
    • 3
  1. 1.University of GroningenGroningenNetherlands
  2. 2.Eindhoven University of TechnologyEindhovenNetherlands
  3. 3.Tilburg UniversityTilburgNetherlands

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