Change pattern relationships in event logs

Abstract

Process mining utilises process execution data to discover and analyse business processes. Event logs represent process executions, providing information about the activities executed. In addition to generic event attributes like activity name and timestamp, events might contain domain-specific attributes, such as a blood sugar measurement in a healthcare environment. Many of these values change during a typical process quite frequently. We refer to those as dynamic event attributes. Change patterns can be derived from dynamic event attributes, describing if the attribute values change from one activity to another. So far, change patterns can only be identified in an isolated manner, neglecting the chance of finding co-occuring change patterns. This paper provides an approach to identifying relationships between change patterns by utilising correlation methods from statistics. We applied the proposed technique on two event logs derived from the MIMIC-IV real-world dataset on hospitalisations in the US and evaluated the results with a medical expert. It turns out that relationships between change patterns can be detected within the same directly or eventually follows relation and even beyond that. Further, we identify unexpected relationships that are occurring only at certain parts of the process. Thus, the process perspective reveals novel insights on how dynamic event attributes change together during process execution. The approach is implemented in Python using the PM4Py framework.