Within the process mining field, Deterministic Finite State Automata (DFAs) are largely employed as foundation mechanisms to perform formal reasoning tasks over the information contained in the event logs, such as conformance checking, compliance monitoring and cross-organization process analysis, just to name a few. To support the above use cases, in this paper, we investigate how to leverage Model Learning (ML) algorithms for the automated discovery of DFAs from event logs. DFAs can be used as a fundamental building block to support not only the development of process analysis techniques, but also the implementation of instruments to support other phases of the Business Process Management (BPM) lifecycle such as business process design and enactment. The quality of the discovered DFAs is assessed wrt customized definitions of fitness, precision, generalization, and a standard notion of DFA simplicity. Finally, we use these metrics to benchmark ML algorithms against real-life and synthetically generated datasets, with the aim of studying their performance and investigate their suitability to be used for the development of BPM tools.

Process mining meets model learning: Discovering deterministic finite state automata from event logs for business process analysis / Agostinelli, Simone; Chiariello, Francesco; Maggi, FABRIZIO MARIA; Marrella, Andrea; Patrizi, Fabio. - In: INFORMATION SYSTEMS. - ISSN 0306-4379. - 114:(2023). [10.1016/j.is.2023.102180]

Process mining meets model learning: Discovering deterministic finite state automata from event logs for business process analysis

Simone Agostinelli;Francesco Chiariello;Fabrizio Maria Maggi;ANDREA MARRELLA
;
Fabio Patrizi
2023

Abstract

Within the process mining field, Deterministic Finite State Automata (DFAs) are largely employed as foundation mechanisms to perform formal reasoning tasks over the information contained in the event logs, such as conformance checking, compliance monitoring and cross-organization process analysis, just to name a few. To support the above use cases, in this paper, we investigate how to leverage Model Learning (ML) algorithms for the automated discovery of DFAs from event logs. DFAs can be used as a fundamental building block to support not only the development of process analysis techniques, but also the implementation of instruments to support other phases of the Business Process Management (BPM) lifecycle such as business process design and enactment. The quality of the discovered DFAs is assessed wrt customized definitions of fitness, precision, generalization, and a standard notion of DFA simplicity. Finally, we use these metrics to benchmark ML algorithms against real-life and synthetically generated datasets, with the aim of studying their performance and investigate their suitability to be used for the development of BPM tools.
2023
Model learning; Deterministic finite state automata; Process mining quality metrics
01 Pubblicazione su rivista::01a Articolo in rivista
Process mining meets model learning: Discovering deterministic finite state automata from event logs for business process analysis / Agostinelli, Simone; Chiariello, Francesco; Maggi, FABRIZIO MARIA; Marrella, Andrea; Patrizi, Fabio. - In: INFORMATION SYSTEMS. - ISSN 0306-4379. - 114:(2023). [10.1016/j.is.2023.102180]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1667983
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