Business process similarity computing method based on process model structure and log behavior

被引：0
|
作者：
Zhou C. [1 ,2 ]
Zeng Q. [2 ]
Liu C. [2 ]
Duan H. [3 ]
Yuan G. [2 ]
机构：
[1] College of Economics and Management, Shandong University of Science and Technology, Qingdao
[2] College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao
[3] College of Mathematics and System Science, Shandong University of Science and Technology, Qingdao
来源：
Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS | 2018年 / 24卷 / 07期
基金：
中国国家自然科学基金;
关键词：
Business process; Log behavior; Process model structure; Similarity computing; Weighted graph edit distance;
D O I：
10.13196/j.cims.2018.07.021
中图分类号：
学科分类号：
摘要：
Business process similarity computation is an indispensable task of process management. A novel approach was proposed to compute the process similarity more accurately by considering both structure and behavior characteristics. Process models were converted to Business Process Graphs (BPG). The weight to each edge in the BPG was added according to the event log to obtain a weighted BPG. The weighted BPG that integrated the behavior of both process model and event log could provide more reliable and comprehensive similarity measure. The edit distance measure of two weighed BPGs was defined to compute the similarity. By comparison with existing approaches, the effectiveness of the proposed approach was demonstrated, and all approaches had been implemented in the open-source process mining toolkit ProM. © 2018, Editorial Department of CIMS. All right reserved.
引用
收藏
页码：1793 / 1805
页数：12
相关论文
共 32 条
  • [1] Dumas M., Garcia B.L., Dijkman R.M., Similarity search of business process models, IEEE Data Engineering Bulletin, 32, 3, pp. 23-28, (2009)
  • [2] Dijkman R., Dumas M., Van D.B., Et al., Similarity of business process models: Metrics and evaluation, Information Systems, 36, 2, pp. 498-516, (2011)
  • [3] Akkiraju R., Ivan A., Discovering business process similarities: an empirical study with SAP best practice business processes, Proceedings of the International Conference onService-Oriented Computing, pp. 515-526, (2010)
  • [4] Ehrig M., Koschmider A., Oberweis A., Measuring similarity between semantic business process models, Proceedings of the 4th Asia-Pacific Conference on Conceptual Modelling, pp. 71-80, (2007)
  • [5] Gao X., Xiao B., Tao D., Et al., A survey of graph edit distance, Pattern Analysis and Applications, 13, 1, pp. 113-129, (2010)
  • [6] Dijkman R., Dumas M., Garcia B.L., Graph matching algorithms for business process model similarity Search, Notes in Computer Science, 57, 1, pp. 48-63, (2009)
  • [7] Zeng Q., Zhou C., Lu F., Et al., Multi-view-based cross-organizational emergency response process similarity computation, Computer Integrated Manufacturing Systems, 21, 2, pp. 368-380, (2015)
  • [8] Kunze M., Weske M., Metric trees for efficient similarity search in large process model repositories, Proceedings of the International Conference on Business Process Management, pp. 535-546, (2010)
  • [9] Wang Z., Wen L., Wang S., Et al., Similarity measurement for process models based on transition-labeled graph edit distance, Computer Integrated Manufacturing Systems, 22, 2, pp. 343-352, (2016)
  • [10] Nan J., Fu X., Huang Y., Et al., Workflow distance metric based on tree edit distance, Journal of Computer Applications, 32, 12, pp. 3529-3533, (2012)
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