This article tackles the problem of discovering a process model from an event log recording the execution of tasks in a business process. Previous approaches to this reverse-engineering problem strike different tradeoffs between the accuracy of the discovered models and their structural complexity. With respect to the latter property, empirical studies have demonstrated that block-structured process models are generally more understandable and less error-prone than unstructured ones. Accordingly, several methods for automated process model discovery generate block-structured models only. These methods however intertwine the objective of producing accurate models with that of ensuring their structuredness, and often sacrifice the former in favour of the latter. In this paper we propose an alternative approach that separates these concerns. Instead of directly discovering a structured process model, we first apply a well-known heuristic that discovers accurate but oftentimes unstructured (and even unsound) process models, and then we transform the resulting process model into a structured (and sound) one. An experimental evaluation on synthetic and real-life event logs shows that this discover-and-structure approach consistently outperforms previous approaches with respect to a range of accuracy and complexity measures.

Automated discovery of structured process models from event logs: the discover-and-structure approach / Augusto, Adriano; Conforti, Raffaele; Dumas, Marlon; La Rosa, Marcello; Bruno, Giorgio. - In: DATA & KNOWLEDGE ENGINEERING. - ISSN 0169-023X. - STAMPA. - 117:(2018), pp. 373-392. [10.1016/j.datak.2018.04.007]

Automated discovery of structured process models from event logs: the discover-and-structure approach

Giorgio Bruno
2018

Abstract

This article tackles the problem of discovering a process model from an event log recording the execution of tasks in a business process. Previous approaches to this reverse-engineering problem strike different tradeoffs between the accuracy of the discovered models and their structural complexity. With respect to the latter property, empirical studies have demonstrated that block-structured process models are generally more understandable and less error-prone than unstructured ones. Accordingly, several methods for automated process model discovery generate block-structured models only. These methods however intertwine the objective of producing accurate models with that of ensuring their structuredness, and often sacrifice the former in favour of the latter. In this paper we propose an alternative approach that separates these concerns. Instead of directly discovering a structured process model, we first apply a well-known heuristic that discovers accurate but oftentimes unstructured (and even unsound) process models, and then we transform the resulting process model into a structured (and sound) one. An experimental evaluation on synthetic and real-life event logs shows that this discover-and-structure approach consistently outperforms previous approaches with respect to a range of accuracy and complexity measures.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2709937
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