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 incorporate both the behavioral and label information of processes for the identification of correspondences between activities. Given two business process models, we achieve our goal by defining an integer linear program which maximizes the label similarities among process activities and the behavioral similarity between the process models. Our approach enables 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 parameter, allowing for flexibility. Moreover, extensive experimental evaluation performed on three real-world datasets points out the high accuracy of our proposal, outperforming the state of the art.
Highlights
The ubiquity of advanced capabilities of the digital world enables organizations to generate and store process models which exhibit indispensable activities of their business processes in various domains, e.g., finance, l o gistics, a nd p roduction [ 1, 1 4, 18]
We introduce a novel business process model matching approach Optimization-based Process Model Matching (OPTIMA) which matches the individual components of two
By inspecting the plots in both figures, we note that micro and macro aggregated evaluation results of all approaches expose a similar tendency over utilized real-world datasets (Birth, Asset, and University), as anticipated
Summary
The ubiquity of advanced capabilities of the digital world enables organizations to generate and store process models which exhibit indispensable activities of their business processes in various domains, e.g., finance, l o gistics, a nd p roduction [ 1, 1 4, 18]. Most of the existing model matching techniques typically utilize activity labels and process structures to determine process matching in model repositories [12]. We introduce a novel business process model matching approach Optimization-based Process Model Matching (OPTIMA) which matches the individual components of two. Our proposal exhibits an optimization problem which maximizes the activity label similarities at an individual local level, and simultaneously maximizes the behavioral similarity of the given processes at a global level by utilizing their relational profiles [19, 21, 22, 24]. Our paper is structured as follows: Section 2 gives an overview of the related work regarding business process model matching.
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