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Business Process Modeling Abstraction Based on Semi-Supervised Clustering Analysis

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Abstract

The most prominent Business Process Model Abstraction (BPMA) use case is the construction of the process “quick view” for rapidly comprehending a complex process. Some researchers propose process abstraction methods to aggregate the activities on the basis of their semantic similarity. One important clustering technique used in these methods is traditional k-means cluster analysis which so far is an unsupervised process without any priori information, and most of the techniques aggregate the activities only according to business semantics without considering the requirement of an order-preserving model transformation. The paper proposes a BPMA method based on semi-supervised clustering which chooses the initial clusters based on the refined process structure tree and designs constraints by combining the control flow consistency of the process and the semantic similarity of the activities to guide the clustering process. To be more precise, the constraint function is discovered by mining from a process model collection enriched with subprocess relations. The proposed method is validated by applying it to a process model repository in use. In an experimental validation, the proposed method is compared to the traditional k-means clustering (parameterized with randomly chosen initial clusters and an only semantics-based distance measure), showing that the approach closely approximates the decisions of the involved modelers to cluster activities. As such, the paper contributes to the development of modeling support for effective process model abstraction, facilitating the use of business process models in practice.

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Acknowledgements

This work is supported in part by NSFC under Grant Nos. 61402193, 61272208, 61133011, 60973089, 61003101, 61170092, by the Jilin Province Science and Technology Development Plan under Grant Nos. 20130522177JH, by the Jilin Provincial Department of Education “Twelfth/Thirteenth Five Year Plan” Science and Technology Development Plan under Grant Nos. 2014160, 2016105, and by the Jilin Province Education Science “Twelfth Five Year Plan” under Grant Nos. GH150285, GH16249.

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Authors and Affiliations

  1. Department of Management Science and Information Engineering, Laboratory of Logistics Industry Economy and Intelligent Logistics, Jilin University of Finance and Economics, Changchun, 130117, China

    Nan Wang & Shanwu Sun

  2. Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China

    Dantong OuYang

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  1. Nan Wang
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  2. Shanwu Sun
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Correspondence to Shanwu Sun.

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Accepted after three revisions by Prof. Dr. Becker.

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Wang, N., Sun, S. & OuYang, D. Business Process Modeling Abstraction Based on Semi-Supervised Clustering Analysis. Bus Inf Syst Eng 60, 525–542 (2018). https://doi.org/10.1007/s12599-016-0457-x

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