Abstract
Discovering and analysing business processes are important tasks for organizations. 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 like case identifier, activity, and timestamp, additional event attributes can be present, such as human resources, costs, and laboratory values. These event attributes can be modified by multiple events in a trace, which can be classified as so-called dynamic event attributes. So far, the process behaviour of event attributes is described in the form of read/write operations or object-lifecycle states. However, the actual value behaviour has not been considered yet. This paper introduces an approach that allows to automatically detect changes in the actual values of dynamic event attributes, enabling to identify changes between process activities representing events with the same activity name. This can help to confirm expected behaviour of dynamic event attributes, but also allows deriving novel insights by identifying unexpected changes. We applied the proposed technique on the MIMIC-IV real-world data set on hospitalizations in the US and evaluated the results together with a medical expert. The approach is implemented in Python with the help of the PM4Py framework.
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Cremerius, J., Weske, M. (2022). Change Detection in Dynamic Event Attributes. In: Di Ciccio, C., Dijkman, R., del Río Ortega, A., Rinderle-Ma, S. (eds) Business Process Management Forum. BPM 2022. Lecture Notes in Business Information Processing, vol 458. Springer, Cham. https://doi.org/10.1007/978-3-031-16171-1_10
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