In healthcare research, the focus is on complex, patient-related healthcare processes that often extend over years and are provided by different service providers. These must first be determined and described - usually manually - in compliance with data protection regulations.

Process mining is an alternative to the manual recording of healthcare processes. This refers to techniques for automatically extracting, analysing and manipulating process models. Among other things, these are able to automatically extract corresponding models that describe the observed behaviour from the event data (event logs) recorded during the execution of processes. However, as the event logs are flat tables that contain all the events recorded for a process, process mining initially only provides an overall view of the process. For healthcare research, however, a differentiated analysis of different patient groups (e.g. with different ages or pre-existing conditions)
is of particular interest. In addition, it is usually not known in advance what the differences between the individual patient groups might be. Therefore, an explorative analysis of the processes is desirable, which enables a user to analyse and compare different patient groups step by step.

The basic idea behind the multidimensional process mining approach is that the properties of the patient groups, which appear as attributes in the event log, can be interpreted as dimensions that together form a multidimensional data cube. OLAP operations can then be used to define any patient groups using corresponding data extracts as the data basis for process mining. This allows separate models to be created for the different patient groups, which can then be compared and contrasted by the user. However, if the expected differences are not found, the user can adapt the OLAP queries and define other patient groups accordingly, such as a differentiation according to previous illnesses instead of age. This allows the user to view the care processes step by step from different angles and ultimately identify anomalies. These can then serve as a starting point for further analyses, for example by creating new research hypotheses.

The aim of the PMCube project is to develop new approaches for multidimensional process mining and to investigate their applicability in healthcare research. To this end, the approaches will be implemented in a prototype tool. For evaluation purposes, the tool will be used in
practice-relevant scenarios in healthcare research and used to answer pre-selected questions.