Feature Recommendation for Structural Equation Model Discovery in Process Mining

TL;DR

This paper introduces a method for selecting relevant features and discovering structural equation models in process mining to improve root cause analysis, validated through experiments on real and synthetic data.

Contribution

It presents a novel approach for feature recommendation and structural equation model discovery specifically tailored for process mining applications.

Findings

Method effectively identifies relevant features for root cause analysis.

Structural equation models help distinguish causation from correlation.

Validated on real and synthetic event logs, showing promising results.

Abstract

Process mining techniques can help organizations to improve their operational processes. Organizations can benefit from process mining techniques in finding and amending the root causes of performance or compliance problems. Considering the volume of the data and the number of features captured by the information system of today's companies, the task of discovering the set of features that should be considered in root cause analysis can be quite involving. In this paper, we propose a method for finding the set of (aggregated) features with a possible effect on the problem. The root cause analysis task is usually done by applying a machine learning technique to the data gathered from the information system supporting the processes. To prevent mixing up correlation and causation, which may happen because of interpreting the findings of machine learning techniques as causal, we propose a method for discovering the structural equation model of the process that can be used for root cause analysis. We have implemented the proposed method as a plugin in ProM and we have evaluated it using two real and synthetic event logs. These experiments show the validity and effectiveness of the proposed methods.