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Multidimensional Process Mining Using Process Cubes

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Enterprise, Business-Process and Information Systems Modeling (BPMDS 2015, EMMSAD 2015)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 214))

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.

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Author information

Authors and Affiliations

  1. Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, The Netherlands

    Alfredo Bolt & Wil M. P. van der Aalst

Authors
  1. Alfredo Bolt
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  2. Wil M. P. van der Aalst
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Corresponding author

Correspondence to Alfredo Bolt .

Editor information

Editors and Affiliations

  1. Luxembourg Institute of Science & Technology, Luxembourg, Luxembourg

    Khaled Gaaloul

  2. Munich University of Applied Sciences, Munich, Germany

    Rainer Schmidt

  3. Université Paris 1 Panthéon – Sorbonne, Paris, France

    Selmin Nurcan

  4. Lusofona University, Lisbon, Portugal

    Sérgio Guerreiro

  5. Luxembourg Institute of Science & Technology, Luxembourg, Luxembourg

    Qin Ma

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Bolt, A., van der Aalst, W.M.P. (2015). Multidimensional Process Mining Using Process Cubes. In: Gaaloul, K., Schmidt, R., Nurcan, S., Guerreiro, S., Ma, Q. (eds) Enterprise, Business-Process and Information Systems Modeling. BPMDS EMMSAD 2015 2015. Lecture Notes in Business Information Processing, vol 214. Springer, Cham. https://doi.org/10.1007/978-3-319-19237-6_7

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  • DOI: https://doi.org/10.1007/978-3-319-19237-6_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-19236-9

  • Online ISBN: 978-3-319-19237-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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