Automated Repair of Process Models with Non-local Constraints Using State-Based Region Theory
| Autor: | Kalenkova, Anna, Carmona, Josep, Polyvyanyy, Artem, La Rosa, Marcello |
|---|---|
| Přispěvatelé: | Universitat Politècnica de Catalunya. Departament de Ciències de la Computació, Universitat Politècnica de Catalunya. ALBCOM - Algorísmia, Bioinformàtica, Complexitat i Mètodes Formals |
| Rok vydání: | 2022 |
| Předmět: |
FOS: Computer and information sciences
Event logs Free-choice Petri nets Process enhancement Algebra and Number Theory Formal Languages and Automata Theory (cs.FL) Computer Science - Artificial Intelligence Petri nets Computer Science - Formal Languages and Automata Theory Region state-based synthesis Transition systems Theoretical Computer Science Artificial Intelligence (cs.AI) Informàtica::Informàtica teòrica [Àrees temàtiques de la UPC] Computational Theory and Mathematics Petri Xarxes de Process mining Mineria de dades Data mining Information Systems |
| Zdroj: | Fundamenta Informaticae. 183:293-317 |
| ISSN: | 1875-8681 0169-2968 |
| DOI: | 10.3233/fi-2021-2089 |
| Popis: | State-of-the-art process discovery methods construct free-choice process models from event logs. Consequently, the constructed models do not take into account indirect dependencies between events. Whenever the input behaviour is not free-choice, these methods fail to provide a precise model. In this paper, we propose a novel approach for enhancing free-choice process models by adding non-free-choice constructs discovered a-posteriori via region-based techniques. This allows us to benefit from the performance of existing process discovery methods and the accuracy of the employed fundamental synthesis techniques. We prove that the proposed approach preserves fitness with respect to the event log while improving the precision when indirect dependencies exist. The approach has been implemented and tested on both synthetic and real-life datasets. The results show its effectiveness in repairing models discovered from event logs. This work was partly supported by the Australian Research Council Discovery Project DP180102839. This work was supported by MINECO and FEDER funds under grant TIN2017-86727-C2-1-R. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|