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Process Mining in the Large: A Tutorial

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Business Intelligence (eBISS 2013)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 172))

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Abstract

Recently, process mining emerged as a new scientific discipline on the interface between process models and event data. On the one hand, conventional Business Process Management (BPM) and Workflow Management (WfM) approaches and tools are mostly model-driven with little consideration for event data. On the other hand, Data Mining (DM), Business Intelligence (BI), and Machine Learning (ML) focus on data without considering end-to-end process models. Process mining aims to bridge the gap between BPM and WfM on the one hand and DM, BI, and ML on the other hand. Here, the challenge is to turn torrents of event data (“Big Data”) into valuable insights related to process performance and compliance. Fortunately, process mining results can be used to identify and understand bottlenecks, inefficiencies, deviations, and risks. This tutorial paper introduces basic process mining techniques that can be used for process discovery and conformance checking. Moreover, some very general decomposition results are discussed. These allow for the decomposition and distribution of process discovery and conformance checking problems, thus enabling process mining in the large.

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Notes

  1. 1.

    As will be shown later, there are different ways of measuring the quality of a process discovery result. The term “adequately” is just an informal notion that will be detailed later.

  2. 2.

    Note that “fitness” in genetic mining has a different meaning than the (replay) fitness at other places in this paper. Genetic fitness corresponds to the more general notion of conformance including replay fitness, simplicity, precision, and generalization.

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Acknowledgements

This work was supported by the Basic Research Program of the National Research University Higher School of Economics (HSE).

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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. Université Libre de Bruxelles, Brussels, Belgium

    Esteban Zimányi

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van der Aalst, W.M. (2014). Process Mining in the Large: A Tutorial. In: Zimányi, E. (eds) Business Intelligence. eBISS 2013. Lecture Notes in Business Information Processing, vol 172. Springer, Cham. https://doi.org/10.1007/978-3-319-05461-2_2

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