Multidimensional process mining using process cubes

Conference Contribution

Bolt, A. & Aalst, van der, W.M.P. (2015). Multidimensional process mining using process cubes. In Q. Ma, S. Nurcan, S. Guerreiro, K. Gaalouli & R. Schmidt (Eds.), Enterprise, Business-Process and Information Systems Modeling (16th International Conference, BPMDS 2015, 20th International Conference, EMMSAD 2015, Held at CAiSE 2015, Stockholm, Sweden, June 8-9, 2015, Proceedings) (pp. 102-116). (Lecture Notes in Business Information Processing, No. 214). Berlin: Springer. In Scopus Cited 14 times.

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Abstract

 

Process mining techniques enable the analysis of processes using event data. For structured processes without too many variations, it is possible to show a relative simple model and project performance and conformance information on it. However, if there are multiple classes of cases exhibiting markedly different behaviors, then the overall process will be too complex to interpret. Moreover, it will be impossible to see differences in performance and conformance for the different process variants. The different process variations should be analysed separately and compared to each other from different perspectives to obtain meaningful insights about the different behaviors embedded in the process. This paper formalizes the notion of process cubes where the event data is presented and organized using different dimensions. Each cell in the cube corresponds to a set of events which can be used as an input by any process mining technique. This notion is related to the well-known OLAP (Online Analytical Processing) data cubes, adapting the OLAP paradigm to event data through multidimensional process mining. This adaptation is far from trivial given the nature of event data which cannot be easily summarized or aggregated, conflicting with classical OLAP assumptions. For example, multidimensional process mining can be used to analyze the different versions of a sales processes, where each version can be defined according to different dimensions such as location or time, and then the different results can be compared. This new way of looking at processes may provide valuable insights for process optimization.

Keywords: Process cube; Process mining; OLAP; Comparative process mining