Transition Adjacency Relation Computation Based on Unfolding: Potentials and Challenges

Pei, Jisheng; Wen, Lijie; Yao, Xiaojun; Kumar, Akhil; Lin, Zijing

doi:10.1007/978-3-319-48472-3_4

Cited by 1 publication

(1 citation statement)

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“…This paper is an extended version of our previous work [15], but with significant amount of new contributions to the problem of transition adjacency relation (TAR) computation. A new type of cut-off criteria for constructing finite prefixes of Petri net unfolding has been provided in this paper to remove the necessity of costly coverability checking operation, which has been identified in our previous work [15] a major challenge to TAR computation. More indepth proofs and experiments have been provided in this manuscript to make the proposed TAR/pTAR computation techniques more solid.…”

Section: Introductionmentioning

confidence: 99%

Efficient Transition Adjacency Relation Computation for Process Model Similarity

Pei

Wen

Yao

et al. 2022

IEEE Trans. Serv. Comput.

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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.

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Section: Introductionmentioning

confidence: 99%