A Graph Approach for Enhancing Process Models Matchmaking

Abstract

Recent attempts have been done to measure similarity of process models based on graph-edit distance. This problem is known to be difficult and computational complexity of exact algorithms for graph matching is exponential. Thus, heuristics should be proposed to obtain approximations. Spectral graph matching methods, in particular eigenvalue-based projections, are know to be fast but they lost some quality in the obtained matchmaking. In this paper, we propose a graph approach for the problem of inexact matching of process models. Our approach combines a spectral graph matching method and a string comparator based algorithm in order to improve the quality of process model matchmaking. The proposed method performs the matchmaking at both structural and semantic levels. Experimentation is provided to show the performance of our method, compared to previous work, to rank a collection of process model according to a particular query.