Optimization-based Business Process Model Matching

Abstract

The rapid increase in generation of business process models in the industry has raised the demand on the development of process model matching approaches. In this paper, we introduce a novel optimization-based business process model matching approach which can flexibly i ncorporate both t he behavioral and l abel i nformation of processes f or t he identifi-cation of correspondences between activities. Given two business process models, we achieve our goal by defining a n i nteger l inear p rogram w hich m aximizes t he l abel s imilarities among process activities and the behavioral similarity between the process models. Our approach en-ables the user to determine the importance of the local label-based similarities and the global behavioral similarity of the models by offering the utilization of a predefined weighting param-eter, allowing for flexibility. M oreover, e xtensive e xperimental e valuation p erformed o n three real-world datasets points out the high accuracy of our proposal, outperforming the state of the art.