Transforming Dynamic Condition Response Graphs to Safe Petri Nets

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We present a transformation of the Dynamic Condition Response (DCR) graph constraint based process specification language to safe Petri Nets with inhibitor and read arcs, generalized with an acceptance criteria enabling the specification of the union of regular and ω -regular languages. We prove that the DCR graph and the resulting Petri Net are bisimilar and that the bisimulation respects the acceptance criterium. The transformation enables the capturing of regular and omega-regular process requirements from texts and event logs using existing tools for DCR requirements mapping and process mining. A representation of DCR Graphs as Petri Nets advances the understanding of the relationship between the two models and enables improved analysis and model checking capabilities for DCR graph specifications through mature Petri net tools. We provide a python script implementing the transformation from the DCR XML export format to the PNML exchange format extended with arc types. In the implementation, all read arcs are replaced by a pair of standard input and output arcs. This directly enables the simulation and analysis of the resulting Petri Nets in tools such as TAPAAL, but means that the acceptance criterium for infinite runs is not preserved.

Original languageEnglish
Title of host publicationApplication and Theory of Petri Nets and Concurrency - 44th International Conference, PETRI NETS 2023, Proceedings
EditorsLuis Gomes, Robert Lorenz
Number of pages23
PublisherSpringer
Publication date2023
Pages417-439
ISBN (Print)9783031336195
DOIs
Publication statusPublished - 2023
Event44th International Conference on Application and Theory of Petri Nets and Concurrency, PETRI NETS 2023 - Lisbon, Portugal
Duration: 25 Jun 202330 Jun 2023

Conference

Conference44th International Conference on Application and Theory of Petri Nets and Concurrency, PETRI NETS 2023
LandPortugal
ByLisbon
Periode25/06/202330/06/2023
SeriesLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13929 LNCS
ISSN0302-9743

Bibliographical note

Funding Information:
Acknowledgment. A partial support by the Discovery NSERC of Canada grant No. 6466-15, and the Leverhulme Trust grant RPG-2022-025 is acknowledged. The authors gratefully acknowledge four anonymous referees, whose comments significantly contributed to the final version of this paper.

Funding Information:
Acknowledgments. We thank the anonymous reviewers for their insightful comments. Arias, Olarte, Ölveczky, Petrucci, and Rømming acknowledge support from CNRS INS2I project ESPRiTS and the PHC project Aurora AESIR. Bae was supported by the NRF grants funded by the Korea government (No. 2021R1A5A1021944 and No. 2022R1F1A1074550).

Funding Information:
Acknowledgments. The authors thank the Alexander von Humboldt (AvH) Stiftung for supporting this research. Funded by the Deutsche Forschungsgemeinschaft (DFG) under Germany’s Excellence Strategy, Internet of Production (390621612).

Funding Information:
Acknowledgements. This work is supported by the National Science Centre, Poland, under Grant number 2019/35/B/ST6/01683.

Funding Information:
Work supported in part by National Science Foundation under grant CCF-2212142.

Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

    Research areas

  • Bisimilarity, DCR graphs, Petri Nets

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