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A Repair of Workflow Models Based on Mirroring Matrices

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Abstract

Process models can be discovered from event logs and conformance checking techniques can be used to detect and diagnose the differences between observed and modeled behaviors. Some existing conformance checking techniques have been used for repairing a process model, but there are also many techniques can uncover these differences efficiently. So we can use these techniques to repair a process model. In this paper we research the problem of repairing the workflow models w.r.t. the corresponding real process has been changed and the change is adding new activities. To solve this problem, a mirroring matrix is presented based on the variant of footprints and parallel programs. The mirroring matrix is a uniform standard to compare an original model with a complete event log gained from the corresponding real process. Based on this information, the differences between a model and a log can be found. We suggest four algorithms that make model repair realized. Finally, the effectiveness and correctness of the proposed techniques are illustrated via contrastive experiments and analysis.

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Acknowledgments

This work is supported by the National Natural Science Foundation of China under Grants 61170078 and 61472228; the Taishan Scholar Construction Project of Shandong Province, China; the Natural Science Foundation of Shandong province under Grant ZR2014FM009; and the Promotive research fund for young and middle-aged scientists of Shandong Province under Grant BS2015DX010.

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Authors and Affiliations

  1. College of Information Science and Engineering, Shandong University of Science and Technology, Qingdao, 266590, China

    Yanan Sun & Yuyue Du

  2. Department of Electronic and Computer Engineering, Brunel University London, Uxbridge, UB8 3PH, UK

    Maozhen Li

Authors
  1. Yanan Sun
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  2. Yuyue Du
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  3. Maozhen Li
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Correspondence to Yuyue Du.

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Sun, Y., Du, Y. & Li, M. A Repair of Workflow Models Based on Mirroring Matrices. Int J Parallel Prog 45, 1001–1020 (2017). https://doi.org/10.1007/s10766-016-0438-1

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  • DOI: https://doi.org/10.1007/s10766-016-0438-1

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