Efficient Transition Adjacency Relation Computation for Process Model Similarity

Jisheng Pei, Lijie Wen, Xiaojun Ye, Akhil Kumar

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)1295-1308
Number of pages14
JournalIEEE Transactions on Services Computing
Issue number3
StatePublished - 2022

All Science Journal Classification (ASJC) codes

  • Hardware and Architecture
  • Computer Science Applications
  • Computer Networks and Communications
  • Information Systems and Management


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