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Workflow Clustering Method Based on Process Similarity

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Computational Science and Its Applications - ICCSA 2006 (ICCSA 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3981))

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Abstract

Process-centric information systems have been accumulating a mount of process models. Process designers continue to create new process models and they long for process analysis tools in various viewpoints. This paper proposes a novel approach of process analysis. Workflow clustering facilitates to analyze accumulated workflow process models and classify them into characteristic groups. The framework consists of two phases: domain classification and pattern analysis. Domain classification exploits an activity similarity measure, while pattern analysis does a transition similarity measure. Process models are represented as weighted complete dependency graphs, and then similarities among their graph vectors are estimated in consideration of relative frequency of each activity and transition. Finally, the models are clustered based on the similarities by a hierarchical clustering algorithm. We implemented the methodology and experimented sets of synthetic processes. Workflow clustering is adaptable to various process analyses, such as workflow recommendation, workflow mining, and process patterns analysis.

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Author information

Authors and Affiliations

  1. Dept. of Technology Management, Eindhoven University of Technology, PO Box 513, 5600 MB, Eindhoven, The Netherlands

    Jae-Yoon Jung

  2. Dept. of Industrial and Information Systems Engineering, Chonbuk National University, 664-14 1ga, Duckjin-dong, Duckjin-gu, Jeonju, Chonbuk, Republic of Korea

    Joonsoo Bae

Authors
  1. Jae-Yoon Jung
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  2. Joonsoo Bae
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Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Calgary, 2500 University Drive N.W., T2N 1N4, Calgary, AB, Canada

    Marina L. Gavrilova

  2. Department of Mathematics and Computer Science, University of Perugia, via Vanvitelli, 1, I-06123, Perugia, Italy

    Osvaldo Gervasi

  3. William Norris Professor, Head of the Computer Science and Engineering Department, University of Minnesota, USA

    Vipin Kumar

  4. OptimaNumerics Ltd., Cathedral House, 23-31 Waring Street, BT1 2DX, Belfast, UK

    C. J. Kenneth Tan

  5. Clayton School of IT, Monash University, 3800, Clayton, Australia

    David Taniar

  6. Department of Chemistry, University of Perugia, Via Elce di Sotto, 8, I-06123, Perugia, Italy

    Antonio Laganá

  7. School of Computing, Soongsil University, Seoul, Korea

    Youngsong Mun

  8. School of Information and Communication Engineering, Sungkyunkwan University, Korea

    Hyunseung Choo

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© 2006 Springer-Verlag Berlin Heidelberg

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Jung, JY., Bae, J. (2006). Workflow Clustering Method Based on Process Similarity. In: Gavrilova, M.L., et al. Computational Science and Its Applications - ICCSA 2006. ICCSA 2006. Lecture Notes in Computer Science, vol 3981. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11751588_40

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  • DOI: https://doi.org/10.1007/11751588_40

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34072-0

  • Online ISBN: 978-3-540-34074-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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