Discovering Sound Free-choice Workflow Nets With Non-block Structures

TL;DR

This paper introduces an automatic method for discovering sound, free-choice process models that are more structurally flexible than traditional block-structured models, using synthesis rules to incrementally build models from event logs.

Contribution

It presents a novel approach that allows for discovering sound, free-choice workflow nets with non-block structures, enhancing expressiveness over existing block-structured methods.

Findings

The approach guarantees soundness and free-choice properties.

It can discover more flexible process models than block-structured methods.

Experimental results show competitive quality of the discovered models.

Abstract

Process discovery aims to discover models that can explain the behaviors of event logs extracted from information systems. While various approaches have been proposed, only a few guarantee desirable properties such as soundness and free-choice. State-of-the-art approaches that exploit the representational bias of process trees to provide the guarantees are constrained to be block-structured.Such constructs limit the expressive power of the discovered models, i.e., only a subset of sound free-choice workflow nets can be discovered. To support a more flexible structural representation, we aim to discover process models that provide the same guarantees but also allow for non-block structures. Inspired by existing works that utilize synthesis rules from the free-choice nets theory, we propose an automatic approach that incrementally adds activities to an existing process model with predefined patterns. Playing by the rules ensures that the resulting models are always sound and free-choice. Furthermore, the discovered models are not restricted to block structures and are thus more flexible. The approach has been implemented in Python and tested using various real-life event logs. The experiments show that our approach can indeed discover models with competitive quality and more flexible structures compared to the existing approach.