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A STRUCTURAL MATCHING APPROACH TO MANAGE LARGE BUSINESS PROCESS MODELS

In this paper, we propose a novel approach that applies structural data mining to the problem of managing a large BPM collection in order to (1) retrieve similar business processes based on metalevel semantics given a context of new process definitions and (2) discover process patterns (and antipatterns) prevalent in the organization’s practice when a domain reference model is being constructed. In the suggested approach, the semantics of BPM specifications are preserved in a conceptual graph format that comprise vertices and edges, along with their instantiations and interrelationships. A structural data mining tool is then applied to deal with a certain degree of informality inherent in business process models while looking at similar sequence patterns based on similarities in sequences of events, processes, and control structures. Our structural matching approach can complement other methods like classification based on descriptors or similarity measures with attributes of business processes. To validate and demonstrate the feasibility and effectiveness of our approach, we conducted a series of experiments with business model collections in a financial service company. The results show that with a modest effort of tuning search process, our approach can be a promising solution for dealing with large process models.