Abstract
Process mining provides a set of techniques to discover process models from recorded event data or to analyze and improve given process models. Typically, these techniques give a single point of view on the process. However, some domains need to differentiate the process according to the characteristic features of their cases. The healthcare domain, for example, needs to distinguish between different groups of patients, defined by the patients’ properties like age or gender, to get more precise insights into the treatment process. The emerging concept of multidimensional process mining aims to overcome this gap by the notion of data cubes that can be used to spread data over multiple cells. This paper introduces PMCube, a novel approach for multidimensional process mining based on the multidimensional modeling of event logs that can be queried by OLAP operators to mine sophisticated process models. An optional step of consolidation allows to reduce the complexity of results to ease its interpretation. We implemented this approach in a prototype and applied it in a case study to analyze the perioperative processes in a large German hospital.
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Acknowledgments
The authors would like to thank Rainer Röhrig, Lena Niehoff, Raphael W. Majeed, and Christian Katzer for their support during the case study.
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Vogelgesang, T., Appelrath, HJ. (2016). PMCube: A Data-Warehouse-Based Approach for Multidimensional Process Mining. In: Reichert, M., Reijers, H. (eds) Business Process Management Workshops. BPM 2016. Lecture Notes in Business Information Processing, vol 256. Springer, Cham. https://doi.org/10.1007/978-3-319-42887-1_14
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DOI: https://doi.org/10.1007/978-3-319-42887-1_14
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