Data-Enhanced Process Models in Process Mining

TL;DR

This paper presents a visualization technique that enhances process models with domain data, enabling more insightful exploration of processes by integrating data attributes into process mining visualizations.

Contribution

It introduces a novel visualization method for data-enhanced process models, bridging the gap between control flow and data in process mining.

Findings

Effective visualization of data-enhanced models demonstrated on MIMIC-IV dataset.

Supports domain experts in exploring data influence on processes.

Enhances understanding of process dynamics through data integration.

Abstract

Understanding and improving business processes have become important success factors for organizations. Process mining has proven very successful with a variety of methods and techniques, including discovering process models based on event logs. Process mining has traditionally focussed on control flow and timing aspects. However, getting insights about a process is not only based on activities and their orderings, but also on the data generated and manipulated during process executions. Today, almost every process activity generates data; these data do not play the role in process mining that it deserves. This paper introduces a visualization technique for enhancing discovered process models with domain data, thereby allowing data-based exploration of processes. Data-enhanced process models aim at supporting domain experts to explore the process, where they can select attributes of interest and observe their influence on the process. The visualization technique is illustrated by the MIMIC-IV real-world data set on hospitalizations in the US.