ProDiff: a process difference detection method based on hierarchical decomposition

Bin Cao, Jiaxing Wang, Jing Fan*, Shuiguang Deng*, Jian Yang, Weiliang Zhao, Jianwei Yin, Mengchu Zhou

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


Detecting and understanding the differences among process models is important for business improvement. Most of the existing work in analysing the differences between two process models employs an edit script approach, i.e., using a sequence of edit operations that transform one to another by applying delete or insert operations. However, describing process differences this way is hard for users to understand and interpret. To overcome the problem, we propose a pattern-based method for process difference detection named ProDiff. We specify a set of process difference patterns as Single-Entry-Single-Exit (SESE) fragments of a process model. Process differences are detected by decomposing process models into different levels of SESE fragments, based on which ProDiff locates the positions of differences and provides assistance for users to carry out further analysis. A case study is provided to show the effectiveness and extensibility of the proposed method.

Original languageEnglish
Pages (from-to)513-526
Number of pages14
JournalIEEE Transactions on Services Computing
Issue number1
Early online date18 Nov 2019
Publication statusPublished - Jan 2022


  • Process model
  • difference detection
  • difference pattern
  • hierarchical decomposition
  • execution time


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