Multidimensional Process Mining with PMCube Explorer.

Abstract

Process mining techniques allow process analysts to generate process models from recorded event logs. Typically, process mining considers the event log as a whole and creates a single model reflecting its behavior. However, the process may be influenced by several characteristics of the process instances, e.g., by the individual characteristics of a patient in the healthcare domain like age and sex. This leads to a wide range of process variations which can end up in complex and confusing models, blurring the behavior of specific process variants. Multidimensional process mining (MPM) aims to overcome this limitation by the notion of data cubes, spreading the data over multiple cells, each representing a group of cases with similar characteristics. This allows for the creation of separated process models for a homogenous set of cases. In this paper, we introduce PMCube Explorer, a novel tool for MPM, that allows for the analysis of a process from various views. It enables the analyst to specify OLAP queries to extract multiple cells from the data warehouse. Each cell contains a subset of event data which are mined separately to discover independent process models. To deal with the potentially high amount of resulting models, our tool provides some distinctive features like the visualization of model differences or the consolidation of multiple process models. We applied our tool in a case study to analyze the perioperative processes in a large German hospital.

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