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Development of Distance Measures for Process Mining, Discovery and Integration

Author

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  • Joonsoo Bae

    (Chonbuk National University, South Korea)

  • Ling Liu

    (Georgia Institute of Technology, USA)

  • James Caverlee

    (Georgia Institute of Technology, USA)

  • Liang-Jie Zhang

    (IBM T.J. Watson Research Center, USA)

  • Hyerim Bae

    (Pusan National University, South Korea)

Abstract

Business processes continue to play an important role in today’s service-oriented enterprise computing systems. Mining, discovering, and integrating process-oriented services has attracted growing attention in the recent years. In this article, we present a quantitative approach to modeling and capturing the similarity and dissimilarity between different process designs. We derive the similarity measures by analyzing the process dependency graphs of the participating workflow processes. We first convert each process dependency graph into a normalized process matrix. Then we calculate the metric space distance between the normalized matrices. This distance measure can be used as a quantitative and qualitative tool in process mining, process merging, and process clustering, and ultimately it can reduce or minimize the costs involved in design, analysis, and evolution of workflow systems.

Suggested Citation

  • Joonsoo Bae & Ling Liu & James Caverlee & Liang-Jie Zhang & Hyerim Bae, 2007. "Development of Distance Measures for Process Mining, Discovery and Integration," International Journal of Web Services Research (IJWSR), IGI Global, vol. 4(4), pages 1-17, October.
    • Handle: RePEc:igg:jwsr00:v:4:y:2007:i:4:p:1-17
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    Cited by:

    1. Valero, Valentín, 2018. "Strong behavioral similarities in timed-arc Petri nets," Applied Mathematics and Computation, Elsevier, vol. 333(C), pages 401-415.

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