Business Process Retrieval From Large Model Repositories for Industry 4.0

Abstract

The process model repository has demonstrated unprecedented success in a variety of industrial and process as a service scenarios. With the rapid increase of massive business process-related data under Industry 4.0, effectively retrieval of process models from large process model repositories becomes a critical challenge for process mining, process deployment and process model acquisition. To accelerate the retrieval of process models from a large process repository, existing retrieval methods rely solely on building single dimension process model indices. In this article we show that this single dimension indexing approach is not only inefficient but also cumbersome for supporting high performance retrieval services over large process model repositories. We propose a new business process model indexing and retrieval with structure and behavior fusion. In the indexing stage, we propose a process model index generation paradigm method with two novel features. First, our index algorithm can transform thetrace equivalent process model(TEPM) with complex structures into a process tree, which can better capture process sequence semantics than the existing approach based on block structured process model. Second, we improve the method for computing the process tree edit distance for measuring process model similarity by introducing the process tree similarity method, which can distinguish leaf nodes and non-leaf nodes and improve the limitations of the traditional edit distance algorithm. Extensive experiments using real world process repositories demonstrate that the proposed methods are under polynomial time in both the model index generation and model querying stages, and offer superior retrieval performance compared to existing process model retrieval methods in terms of efficiency, search capability and scope.

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