Supporting Domain Data Selection in Data-Enhanced Process Models

TL;DR

This paper presents three mechanisms to assist process analysts in selecting relevant domain data attributes from complex event logs, enhancing process models with meaningful domain information for better monitoring and analysis.

Contribution

It introduces novel mechanisms for domain data selection in data-enhanced process models, facilitating targeted analysis of relevant attributes in complex event logs.

Findings

Effective support for domain data selection in process models

Application on MIMIC-IV dataset demonstrates practical utility

Improved monitoring of domain data in process analysis

Abstract

Process mining bridges the gap between process management and data science by discovering process models using event logs derived from real-world data. Besides mandatory event attributes, additional attributes can be part of an event representing domain data, such as human resources and costs. Data-enhanced process models provide a visualization of domain data associated to process activities directly in the process model, allowing to monitor the actual values of domain data in the form of event attribute aggregations. However, event logs can have so many attributes that it is difficult to decide, which one is of interest to observe throughout the process. This paper introduces three mechanisms to support domain data selection, allowing process analysts and domain experts to progressively reach their information of interest. We applied the proposed technique on the MIMIC-IV real-world data set on hospitalizations in the US.