A User Evaluation of Automated Process Discovery Algorithms

TL;DR

This paper systematically evaluates automated process discovery algorithms using real-world logs and expert feedback, revealing gaps and trade-offs to guide future improvements in industry usability.

Contribution

It provides a comparative analysis of existing process discovery algorithms with real-world data and expert evaluation, highlighting areas for enhancement.

Findings

Identified gaps in current process discovery methods.

Revealed trade-offs between different algorithms.

Provided insights for improving industry applicability.

Abstract

Process mining methods allow analysts to use logs of historical executions of business processes in order to gain knowledge about the actual behavior of these processes. One of the most widely studied process mining operations is automated process discovery. An event log is taken as input by an automated process discovery method and produces a business process model as output that captures the control-flow relations between tasks that are described by the event log. In this setting, this paper provides a systematic comparative evaluation of existing implementations of automated process discovery methods with domain experts by using a real-life event log extracted from an international software engineering company and four quality metrics. The evaluation results highlight gaps and unexplored trade-offs in the field and allow researchers to improve the lacks in the automated process discovery methods in terms of usability of process discovery techniques in industry.