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Advanced Process Discovery Techniques

  • Chapter
Process Mining

Abstract

The α-algorithm nicely illustrates some of the main ideas behind process discovery. However, this simple algorithm is unable to manage the trade-offs involving the four quality dimensions described in Chap. 5 (fitness, simplicity, precision, and generalization). To successfully apply process mining in practice, one needs to deal with noise and incompleteness. This chapter focuses on more advanced process discovery techniques. The goal is not to present one particular technique in detail, but to provide an overview of the most relevant approaches. This will assist the reader in selecting the appropriate process discovery technique. Moreover, insights into the strengths and weaknesses of the various approaches support the correct interpretation and effective use of the discovered models.

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Notes

  1. 1.

    Note that in this chapter we again assume that the event log is simple (like in Chap. 5), because at this stage we still abstract from the other perspectives.

  2. 2.

    Note that we overload the term “fitness” in this book. On the one hand, we use it to refer to the ability to replay the event log (see Sects. 5.4.3 and 7.2). On the other hand, we use it for the selection of individuals in genetic process mining. Note that the latter interpretation includes the former, but also adds other elements of the four criteria mentioned in Sect. 5.4.3.

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Authors and Affiliations

  1. Department Mathematics & Computer Science, Eindhoven University of Technology, Den Dolech 2, 5612 AZ, Eindhoven, The Netherlands

    Wil M. P. van der Aalst

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van der Aalst, W.M.P. (2011). Advanced Process Discovery Techniques. In: Process Mining. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19345-3_6

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  • DOI: https://doi.org/10.1007/978-3-642-19345-3_6

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