Transition adjacency relation computation based on unfolding: Potentials and challenges

Jisheng Pei; Lijie Wen; Xiaojun Ye; Akhil Kumar; Zijing Lin

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

Transition adjacency relation computation based on unfolding: Potentials and challenges

Jisheng Pei, Lijie Wen, Xiaojun Ye, Akhil Kumar, Zijing Lin

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Scopus citations

Abstract

Transition Adjacency Relation (TAR) has provided a useful perspective for process model similarity measurement. Motivated by recent developments of other similarity metrics, this article puts TAR computation in the context of Petri net unfolding. Apart from being significantly faster than existing TAR computation algorithms, unfolding based TAR computation also provides the potentials of enhancement through combination with other metrics that can be obtained from unfolding, especially the popular Behavior Profiles. We show that TAR computation can generally be reduced to cover ability problem and solved using unfolding. However, there are also questions to be answered regarding how to further exploit unfolding information for optimal efficiency and handle silent transitions. In this article, we discuss what has been learned from our research, and also point out the open issues.

Original language	English (US)
Title of host publication	On the Move to Meaningful Internet Systems
Subtitle of host publication	OTM 2016 Conferences - Confederated International Conferences: CoopIS, CandTC, and ODBASE 2016, Proceedings
Editors	Tharam Dillon, Christophe Debruyne, Declan Oâ’Sullivan, Herve Panetto, Eva Kuhn, Claudio Agostino Ardagna, Robert Meersman
Publisher	Springer Verlag
Pages	61-79
Number of pages	19
ISBN (Print)	9783319484716
DOIs	https://doi.org/10.1007/978-3-319-48472-3_4
State	Published - 2016
Event	Confederated International Conference On the Move to Meaningful Internet Systems, OTM 2016 held in conjunction with Conferences on CoopIS, CandTC and ODBASE 2016 - Rhodes, Greece Duration: Oct 24 2016 → Oct 28 2016

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10033 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	Confederated International Conference On the Move to Meaningful Internet Systems, OTM 2016 held in conjunction with Conferences on CoopIS, CandTC and ODBASE 2016
Country/Territory	Greece
City	Rhodes
Period	10/24/16 → 10/28/16

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
General Computer Science

Access to Document

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

Cite this

Pei, J., Wen, L., Ye, X., Kumar, A., & Lin, Z. (2016). Transition adjacency relation computation based on unfolding: Potentials and challenges. In T. Dillon, C. Debruyne, D. Oâ’Sullivan, H. Panetto, E. Kuhn, C. A. Ardagna, & R. Meersman (Eds.), On the Move to Meaningful Internet Systems: OTM 2016 Conferences - Confederated International Conferences: CoopIS, CandTC, and ODBASE 2016, Proceedings (pp. 61-79). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10033 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-48472-3_4

Pei, Jisheng ; Wen, Lijie ; Ye, Xiaojun et al. / Transition adjacency relation computation based on unfolding : Potentials and challenges. On the Move to Meaningful Internet Systems: OTM 2016 Conferences - Confederated International Conferences: CoopIS, CandTC, and ODBASE 2016, Proceedings. editor / Tharam Dillon ; Christophe Debruyne ; Declan Oâ’Sullivan ; Herve Panetto ; Eva Kuhn ; Claudio Agostino Ardagna ; Robert Meersman. Springer Verlag, 2016. pp. 61-79 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Transition Adjacency Relation (TAR) has provided a useful perspective for process model similarity measurement. Motivated by recent developments of other similarity metrics, this article puts TAR computation in the context of Petri net unfolding. Apart from being significantly faster than existing TAR computation algorithms, unfolding based TAR computation also provides the potentials of enhancement through combination with other metrics that can be obtained from unfolding, especially the popular Behavior Profiles. We show that TAR computation can generally be reduced to cover ability problem and solved using unfolding. However, there are also questions to be answered regarding how to further exploit unfolding information for optimal efficiency and handle silent transitions. In this article, we discuss what has been learned from our research, and also point out the open issues.",

author = "Jisheng Pei and Lijie Wen and Xiaojun Ye and Akhil Kumar and Zijing Lin",

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Pei, J, Wen, L, Ye, X, Kumar, A & Lin, Z 2016, Transition adjacency relation computation based on unfolding: Potentials and challenges. in T Dillon, C Debruyne, D Oâ’Sullivan, H Panetto, E Kuhn, CA Ardagna & R Meersman (eds), On the Move to Meaningful Internet Systems: OTM 2016 Conferences - Confederated International Conferences: CoopIS, CandTC, and ODBASE 2016, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10033 LNCS, Springer Verlag, pp. 61-79, Confederated International Conference On the Move to Meaningful Internet Systems, OTM 2016 held in conjunction with Conferences on CoopIS, CandTC and ODBASE 2016, Rhodes, Greece, 10/24/16. https://doi.org/10.1007/978-3-319-48472-3_4

Transition adjacency relation computation based on unfolding: Potentials and challenges. / Pei, Jisheng; Wen, Lijie; Ye, Xiaojun et al.
On the Move to Meaningful Internet Systems: OTM 2016 Conferences - Confederated International Conferences: CoopIS, CandTC, and ODBASE 2016, Proceedings. ed. / Tharam Dillon; Christophe Debruyne; Declan Oâ’Sullivan; Herve Panetto; Eva Kuhn; Claudio Agostino Ardagna; Robert Meersman. Springer Verlag, 2016. p. 61-79 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10033 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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T1 - Transition adjacency relation computation based on unfolding

T2 - Confederated International Conference On the Move to Meaningful Internet Systems, OTM 2016 held in conjunction with Conferences on CoopIS, CandTC and ODBASE 2016

AU - Pei, Jisheng

AU - Wen, Lijie

AU - Ye, Xiaojun

AU - Kumar, Akhil

AU - Lin, Zijing

N1 - Funding Information: This work was supported by the General Program of National Natural Science Foundation of China (No. 61472207) and Key Program of Research and Development of MOST (No. 2016YFB1001101). Publisher Copyright: © Springer International Publishing AG 2016.

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N2 - Transition Adjacency Relation (TAR) has provided a useful perspective for process model similarity measurement. Motivated by recent developments of other similarity metrics, this article puts TAR computation in the context of Petri net unfolding. Apart from being significantly faster than existing TAR computation algorithms, unfolding based TAR computation also provides the potentials of enhancement through combination with other metrics that can be obtained from unfolding, especially the popular Behavior Profiles. We show that TAR computation can generally be reduced to cover ability problem and solved using unfolding. However, there are also questions to be answered regarding how to further exploit unfolding information for optimal efficiency and handle silent transitions. In this article, we discuss what has been learned from our research, and also point out the open issues.

AB - Transition Adjacency Relation (TAR) has provided a useful perspective for process model similarity measurement. Motivated by recent developments of other similarity metrics, this article puts TAR computation in the context of Petri net unfolding. Apart from being significantly faster than existing TAR computation algorithms, unfolding based TAR computation also provides the potentials of enhancement through combination with other metrics that can be obtained from unfolding, especially the popular Behavior Profiles. We show that TAR computation can generally be reduced to cover ability problem and solved using unfolding. However, there are also questions to be answered regarding how to further exploit unfolding information for optimal efficiency and handle silent transitions. In this article, we discuss what has been learned from our research, and also point out the open issues.

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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A2 - Oâ’Sullivan, Declan

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PB - Springer Verlag

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Pei J, Wen L, Ye X, Kumar A, Lin Z. Transition adjacency relation computation based on unfolding: Potentials and challenges. In Dillon T, Debruyne C, Oâ’Sullivan D, Panetto H, Kuhn E, Ardagna CA, Meersman R, editors, On the Move to Meaningful Internet Systems: OTM 2016 Conferences - Confederated International Conferences: CoopIS, CandTC, and ODBASE 2016, Proceedings. Springer Verlag. 2016. p. 61-79. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-48472-3_4

Transition adjacency relation computation based on unfolding: Potentials and challenges

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