A Novel Approach to Discover Switch Behaviours in Process Mining

TL;DR

This paper introduces an extension to process mining techniques that effectively detects switch behaviors in process models, enhancing model precision while preserving high fitness, addressing limitations of existing methods.

Contribution

It proposes a novel extension to process trees and the inductive miner to accurately discover switch behaviors in process models, improving precision.

Findings

Model precision improved by 36%

High fitness values maintained

Effective on artificial and real datasets

Abstract

Process mining is a relatively new subject which builds a bridge between process modelling and data mining. An exclusive choice in a process model usually splits the process into different branches. However, in some processes, it is possible to switch from one branch to another. The inductive miner guarantees to return sound process models, but fails to return a precise model when there are switch behaviours between different exclusive choice branches due to the limitation of process trees. In this paper, we present a novel extension to the process tree model to support switch behaviours between different branches of the exclusive choice operator and propose a novel extension to the inductive miner to discover sound process models with switch behaviours. The proposed discovery technique utilizes the theory of a previous study to detect possible switch behaviours. We apply both artificial and publicly-available datasets to evaluate our approach. Our results show that our approach can improve the precision of discovered models by 36% while maintaining high fitness values compared to the original inductive miner.