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Mobile Information Systems
Volume 2017, Article ID 6389567, 17 pages
Research Article

Detecting Difference between Process Models Based on the Refined Process Structure Tree

College of Computer Science and Software Engineering, Zhejiang University of Technology, Hangzhou 310023, China

Correspondence should be addressed to Bin Cao; nc.ude.tujz@oacnib

Received 20 January 2017; Accepted 21 February 2017; Published 15 March 2017

Academic Editor: Jaegeol Yim

Copyright © 2017 Jing Fan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The development of mobile workflow management systems (mWfMS) leads to large number of business process models. In the meantime, the location restriction embedded in mWfMS may result in different process models for a single business process. In order to help users quickly locate the difference and rebuild the process model, detecting the difference between different process models is needed. Existing detection methods either provide a dissimilarity value to represent the difference or use predefined difference template to generate the result, which cannot reflect the entire composition of the difference. Hence, in this paper, we present a new approach to solve this problem. Firstly, we parse the process models to their corresponding refined process structure trees (PSTs), that is, decomposing a process model into a hierarchy of subprocess models. Then we design a method to convert the PST to its corresponding task based process structure tree (TPST). As a consequence, the problem of detecting difference between two process models is transformed to detect difference between their corresponding TPSTs. Finally, we obtain the difference between two TPSTs based on the divide and conquer strategy, where the difference is described by an edit script and we make the cost of the edit script close to minimum. The extensive experimental evaluation shows that our method can meet the real requirements in terms of precision and efficiency.