A Technique for Determining Relevance Scores of Process Activities using Graph-based Neural Networks

TL;DR

This paper introduces Graph Relevance Miner (GRM), a graph neural network-based technique to assign relevance scores to process activities, aiding targeted process analysis and improving business process performance insights.

Contribution

The paper presents a novel graph neural network approach for relevance scoring of process activities, enhancing process mining analysis with quantitative and practical utility.

Findings

High predictive accuracy across four diverse datasets.

Relevance scores improve problem-focused process analysis.

Case study demonstrates practical benefits for organizations.

Abstract

Process models generated through process mining depict the as-is state of a process. Through annotations with metrics such as the frequency or duration of activities, these models provide generic information to the process analyst. To improve business processes with respect to performance measures, process analysts require further guidance from the process model. In this study, we design Graph Relevance Miner (GRM), a technique based on graph neural networks, to determine the relevance scores for process activities with respect to performance measures. Annotating process models with such relevance scores facilitates a problem-focused analysis of the business process, placing these problems at the centre of the analysis. We quantitatively evaluate the predictive quality of our technique using four datasets from different domains, to demonstrate the faithfulness of the relevance scores. Furthermore, we present the results of a case study, which highlight the utility of the technique for organisations. Our work has important implications both for research and business applications, because process model-based analyses feature shortcomings that need to be urgently addressed to realise successful process mining at an enterprise level.