SimBPG: A Comprehensive Similarity Evaluation Metric for Business Process Graphs

Qinkai Jiang, Jiaxing Wang, Bin Cao, Jing Fan

Abstract: Measuring the similarity between two business process models holds significant importance across various applications. At present, there are many different similarity calculation methods, such as structural similarity based on the graph edit distance(GED), text similarity based on task node description, and behavioral similarity calculation based on path matching. However, existing similarity computation methods cannot produce reliable results since: (1) To apply GED, business process graphs will be simplified to homogeneous graph where the heterogeneity as well as the routing semantics of the business process is removed. (2) To derive comprehensive similarity evaluation, linear weighted sum of different similarity metrics is a common way, but the final result strongly depends on the weighting coefficients that are empirically assigned. In this paper, we fuse multidimensional metrics to compensate for the sole reliance on structural similarity based on GED. To address the limitations of comprehensive evaluation, we propose a novel multidimensional process similarity evaluation method based on the entropy weight method and the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method. We also design a experimental method to verify the effectiveness of our method, leveraging an open source dataset. The experiment shows that our method can better represent the similarity of business process graphs than other methods.

External IDs:dblp:conf/colcom/JiangW0F23