Imposing Rules in Process Discovery: an Inductive Mining Approach

TL;DR

This paper introduces a new inductive mining method for process discovery that integrates user-defined rules and domain knowledge to produce more accurate and informative process models from event logs.

Contribution

It presents a novel framework that incorporates rules and domain expertise into process discovery, enhancing traditional inductive mining techniques.

Findings

Effective integration of rules improves model accuracy.

Framework tested on real-life event logs.

Case study demonstrates practical applicability.

Abstract

Process discovery aims to discover descriptive process models from event logs. These discovered process models depict the actual execution of a process and serve as a foundational element for conformance checking, performance analyses, and many other applications. While most of the current process discovery algorithms primarily rely on a single event log for model discovery, additional sources of information, such as process documentation and domain experts' knowledge, remain untapped. This valuable information is often overlooked in traditional process discovery approaches. In this paper, we propose a discovery technique incorporating such knowledge in a novel inductive mining approach. This method takes a set of user-defined or discovered rules as input and utilizes them to discover enhanced process models. Our proposed framework has been implemented and tested using several publicly available real-life event logs. Furthermore, to showcase the framework's effectiveness in a practical setting, we conducted a case study in collaboration with UWV, the Dutch employee insurance agency.