The need to extend business process languages with the capability to model complex data objects along with the control flow perspective has lead to significant practical and theoretical advances in the field of Business Process Modeling (BPM). On the practical side, there are several suites for control flow and data modeling; nonetheless, when it comes to formal verification, the data perspective is abstracted away due to the intrinsic difficulty of handling unbounded data. On the theoretical side, there is significant literature providing decidability results for expressive data-aware processes. However, they struggle to produce a concrete impact as being far from real BPM architectures and, most of all, not providing actual verification tools. In this paper we aim at bridging such a gap: we provide a concrete framework which, on the one hand, being based on Petri Nets and relational models, is close to the widely used BPM suites, and on the other is grounded on solid formal basis whi...

The need to extend business process languages with the capability to model complex data objects along with the control flow perspective has lead to significant practical and theoretical advances in the field of Business Process Modeling (BPM). On the practical side, there are several suites for control flow and data modeling; nonetheless, when it comes to formal verification, the data perspective is abstracted away due to the intrinsic difficulty of handling unbounded data. On the theoretical side, there is significant literature providing decidability results for expressive data-aware processes. However, they struggle to produce a concrete impact as being far from real BPM architectures and, most of all, not providing actual verification tools. In this paper we aim at bridging such a gap: we provide a concrete framework which, on the one hand, being based on Petri Nets and relational models, is close to the widely used BPM suites, and on the other is grounded on solid formal basis which allow to perform formal verification tasks. Moreover, we show how to encode our framework in an action language so as to perform reachability analysis using virtually any state-of-the-art planner.

Add Data into Business Process Verification: Bridging the Gap between Theory and Practice / De Masellis, Riccardo; Di Francescomarino, Chiara; Ghidini, Chiara; Montali, Marco; Tessaris, Sergio. - 13:1: Thirty-First AAAI Conference on Artificial Intelligence(2017), pp. 1091-1099. ( 31st AAAI Conference on Artificial Intelligence, AAAI 2017 San Francisco, California, USA February 4-9, 2017) [10.1609/aaai.v31i1.10688].

Add Data into Business Process Verification: Bridging the Gap between Theory and Practice

Di Francescomarino, Chiara;
2017-01-01

Abstract

The need to extend business process languages with the capability to model complex data objects along with the control flow perspective has lead to significant practical and theoretical advances in the field of Business Process Modeling (BPM). On the practical side, there are several suites for control flow and data modeling; nonetheless, when it comes to formal verification, the data perspective is abstracted away due to the intrinsic difficulty of handling unbounded data. On the theoretical side, there is significant literature providing decidability results for expressive data-aware processes. However, they struggle to produce a concrete impact as being far from real BPM architectures and, most of all, not providing actual verification tools. In this paper we aim at bridging such a gap: we provide a concrete framework which, on the one hand, being based on Petri Nets and relational models, is close to the widely used BPM suites, and on the other is grounded on solid formal basis whi...
2017
Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence
Washington, DC, USA
AAAI PRESS
De Masellis, Riccardo; Di Francescomarino, Chiara; Ghidini, Chiara; Montali, Marco; Tessaris, Sergio
Add Data into Business Process Verification: Bridging the Gap between Theory and Practice / De Masellis, Riccardo; Di Francescomarino, Chiara; Ghidini, Chiara; Montali, Marco; Tessaris, Sergio. - 13:1: Thirty-First AAAI Conference on Artificial Intelligence(2017), pp. 1091-1099. ( 31st AAAI Conference on Artificial Intelligence, AAAI 2017 San Francisco, California, USA February 4-9, 2017) [10.1609/aaai.v31i1.10688].
File in questo prodotto:
File Dimensione Formato  
10688-Article Text-14216-1-2-20201228.pdf

accesso aperto

Tipologia: Versione editoriale (Publisher’s layout)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 776.29 kB
Formato Adobe PDF
776.29 kB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/362615
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 39
  • ???jsp.display-item.citation.isi??? 30
  • OpenAlex ND
social impact