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Transactions on Petri Nets and Other Models of Concurrency XIV pp 1–35Cite as

A Tour in Process Mining: From Practice to Algorithmic Challenges

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Part of the book series: Lecture Notes in Computer Science ((TOPNOC,volume 11790))

Abstract

Process mining seeks the confrontation between modeled behavior and observed behavior. In recent years, process mining techniques managed to bridge the gap between traditional model-based process analysis (e.g., simulation and other business process management techniques) and data-centric analysis techniques such as machine learning and data mining. Process mining is used by many data-driven organizations as a means to improve performance or to ensure compliance. Traditionally, the focus was on the discovery of process models from event logs describing real process executions. However, process mining is not limited to process discovery and also includes conformance checking. Process models (discovered or hand-made) may deviate from reality. Therefore, we need powerful means to analyze discrepancies between models and logs. These are provided by conformance checking techniques that first align modeled and observed behavior, and then compare both. The resulting alignments are also used to enrich process models with performance related information extracted from the event log. This tutorial paper focuses on the control-flow perspective and describes a range of process discovery and conformance checking techniques. The goal of the paper is to show the algorithmic challenges in process mining. We will show that process mining provides a wealth of opportunities for people doing research on Petri nets and related models of concurrency.

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Notes

  1. 1.

    \(a >_L b\) if and only if there is a trace \(\sigma = \langle t_1, t_2, t_3, \ldots t_{n} \rangle \) and \(i\in \{1, \ldots , n-1\}\) such that \(\sigma \in L\) and \(t_i = a\) and \(t_{i+1} = b\).

  2. 2.

    In this paper we will use region to denote a 1-bounded region. However, when needed we will use k-bounded region to extend the notion, necessary to account for k-bounded Petri nets.

  3. 3.

    Remember that we are assuming the standard cost function that assigns cost 1 to synchronous moves and cost 0 to asynchronous moves.

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Acknowledgments

This work has been supported by MINECO and FEDER funds under grant TIN2017-86727-C2-1-R.

Author information

Authors and Affiliations

  1. Process and Data Science Group, RWTH Aachen University, Aachen, Germany

    Wil van der Aalst

  2. Computer Science Department, Universitat Politècnica de Catalunya, Barcelona, Spain

    Josep Carmona

  3. LSV, ENS Paris-Saclay, CNRS, Inria, Université Paris-Saclay, Cachan, France

    Thomas Chatain

  4. Eindhoven University of Technology, Eindhoven, The Netherlands

    Boudewijn van Dongen

Authors
  1. Wil van der Aalst
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  2. Josep Carmona
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  3. Thomas Chatain
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  4. Boudewijn van Dongen
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Corresponding author

Correspondence to Josep Carmona .

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Editors and Affiliations

  1. Newcastle University, Newcastle upon Tyne, UK

    Maciej Koutny

  2. Università degli Studi di Milano, Milan, Italy

    Lucia Pomello

  3. Western Norway University of Applied Sciences, Bergen, Norway

    Lars Michael Kristensen

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van der Aalst, W., Carmona, J., Chatain, T., van Dongen, B. (2019). A Tour in Process Mining: From Practice to Algorithmic Challenges. In: Koutny, M., Pomello, L., Kristensen, L. (eds) Transactions on Petri Nets and Other Models of Concurrency XIV. Lecture Notes in Computer Science(), vol 11790. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-60651-3_1

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