FB-Diff: A Feature Based Difference Detection Algorithm for Process Models

Detecting difference between process models is important for many business process management scenarios, such as process version control and process merging. However, it is far from trivial to detect the process difference. Existing work suffers from drawbacks like inappropriate data structure support or expensive computation. In this paper, we propose FB-Diff, a feature-based difference detection approach. Firstly, a semi-ordered tree model called task based process structure tree (TPST) is used to represent a process model, which can correctly describe the structure as well as the behavior (the execution sequence of task nodes). Then FB-Diff adopts a divide and conquer strategy to find the similar parts of two TPSTs. Specifically, we divide the TPST into fragments that are represented by feature vectors. A feature vector consists of six features, and each feature describes a key characteristic of the fragment. Based on the similar parts, the edit script that can transform one TPST into the other is generated. The extensive experimental evaluation shows that our method can meet the real requirements in terms of precision and efficiency.