Multidimensional process mining: Questions, requirements, and limitations

Multidimensional process mining is an emerging approach that adopts the concept of data cubes to analyze processes from multiple views. This enables analysts to split event logs into a set of homogenous sublogs according to the case and event attributes. Each sublog is independently analyzed using process mining techniques resulting in an individual process model for each sublog. These models can be compared to identify group-related differences between the process variants. In this paper, we derive a number of general research questions addressed for multidimensional process mining by a literature review. We analyze the requirements for its application and point out its limitations and challenges. We conduct two case studies applying multidimensional process mining in two different use cases to evaluate our findings.