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Process Cubes: Slicing, Dicing, Rolling Up and Drilling Down Event Data for Process Mining

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Asia Pacific Business Process Management (AP-BPM 2013)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 159))

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Abstract

Recent breakthroughs in process mining research make it possible to discover, analyze, and improve business processes based on event data. The growth of event data provides many opportunities but also imposes new challenges. Process mining is typically done for an isolated well-defined process in steady-state. However, the boundaries of a process may be fluid and there is a need to continuously view event data from different angles. This paper proposes the notion of process cubes where events and process models are organized using different dimensions. Each cell in the process cube corresponds to a set of events and can be used to discover a process model, to check conformance with respect to some process model, or to discover bottlenecks. The idea is related to the well-known OLAP (Online Analytical Processing) data cubes and associated operations such as slice, dice, roll-up, and drill-down. However, there are also significant differences because of the process-related nature of event data. For example, process discovery based on events is incomparable to computing the average or sum over a set of numerical values. Moreover, dimensions related to process instances (e.g. cases are split into gold and silver customers), subprocesses (e.g. acquisition versus delivery), organizational entities (e.g. backoffice versus frontoffice), and time (e.g., 2010, 2011, 2012, and 2013) are semantically different and it is challenging to slice, dice, roll-up, and drill-down process mining results efficiently.

The original version of this chapter was revised: The copyright line was incorrect. This has been corrected. The Erratum to this chapter is available at DOI: 10.1007/978-3-319-02922-1_10

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Author information

Authors and Affiliations

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

    Wil M. P. van der Aalst

  2. Business Process Management Discipline, Queensland University of Technology, Brisbane, Australia

    Wil M. P. van der Aalst

  3. International Laboratory of Process-Aware Information Systems, National Research University Higher School of Economics, Moscow, Russia

    Wil M. P. van der Aalst

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  1. Wil M. P. van der Aalst
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Editors and Affiliations

  1. School of Technolgy Management,, Ulsan National Institute of Science and Technology, Ulsan, South Korea

    Minseok Song

  2. Information Systems School, Queensland University of Technology, Brisbane, OLD, Australia

    Moe Thandar Wynn

  3. School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, China

    Jianxun Liu

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van der Aalst, W.M.P. (2013). Process Cubes: Slicing, Dicing, Rolling Up and Drilling Down Event Data for Process Mining. In: Song, M., Wynn, M.T., Liu, J. (eds) Asia Pacific Business Process Management. AP-BPM 2013. Lecture Notes in Business Information Processing, vol 159. Springer, Cham. https://doi.org/10.1007/978-3-319-02922-1_1

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  • DOI: https://doi.org/10.1007/978-3-319-02922-1_1

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