Although several approaches have been proposed to compute the similarity between process models, they have various limitations. We propose an approach named TAGER (
dit distance similarity Measu
e) to compute the similarity based on the edit distance between coverability graphs. As the coverability graph represents the behavior of a Petri net well, TAGER, based on it, has a high precise computation. Besides, the T-labeled graphs (an isomorphic graph of the coverability graph) of models are independent, so TAGER can be used as the index for searching process models in a repository. We evaluate TAGER from efficiency and quality perspectives. The results show that TAGER meets all the seven criteria and the distance metric requirement that a good similarity algorithm should have. TAGER also balances the efficiency and precision well.
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