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Efficient Transition Adjacency Relation Computation for Process Model Similarity
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DOI：10.1109/TSC.2020.2984605.png)
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Abstract
En 中文
Many activities in business process management, such as process retrieval, process mining, and process integration, need to determine the similarity between business processes. Along with many other relational behavior semantics, Transition Adjacency Relation (abbr. TAR) has been proposed as a kind of behavioral gene of process models and a useful perspective for process similarity measurement. In this article we explain why it is still relevant and necessary to improve TAR or pTAR (i.e., projected TAR) computation efficiency and put forward a novel approach for TAR computation based on Petri net unfolding. This approach not only improves the efficiency of TAR computation, but also enables the long-expected combined usage of TAR and Behavior Profiles (abbr. BP) in process model similarity estimation.
Keywords:
Computational modeling
Petri nets
Semantics
Estimation
Data models
Task analysis
Business process management
Computational efficiency
Behavioral sciences
Transition adjacency relation
petri net
unfolding
process model similarity
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