A Novel Approach to Discover Switch Behaviours in Process Mining
This addresses a specific limitation in process mining for improving model accuracy in workflows with branch switching, but it is incremental as it builds on existing inductive miner methods.
The paper tackles the problem of discovering switch behaviors between exclusive choice branches in process mining, which the original inductive miner fails to model precisely. The result is a novel extension that improves model precision by 36% while maintaining high fitness.
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.