Feature extraction oriented similarity metric of business process

Abstract:

The existing research of process similarity mostly focuses on process structure but neglects the business semantic, and the similarity calculation on process structure is somehow deficient on computation complexity. To solve this problem, this paper proposes a business process representation with synthetical feature extraction, and a corresponding similarity calculation method is given at the same time. Weight notation of edges is used to extend the process structure for structure feature extraction based on the analysis of basic process control patterns, and high level business semantic is also involved for constructing business process semantic model for semantic feature extraction. The classic similarity metrics of node and edge are heuristically adapted to form a new structure similarity metric, and the similarity of business semantic is computed on vector space model and set theory. The total similarity is deduced by the weighted sum of structure and semantic similarity, and the computation model is self-adaptive to other existing methods by the adjustment of weight assignment. Finally, experiments are carried out to verify the performance of similarity computation, and the results show that the model of this paper is more adaptive and higher in computation efficiency when comparing to other methods in the literatures.