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

Abstract: 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 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">delete</i> or <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">insert</i> 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 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ProDiff</i> . 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 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ProDiff</i> 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.