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A Method for Assessing Parameter Impact on Control-Flow Discovery Algorithms

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Transactions on Petri Nets and Other Models of Concurrency XI

Part of the book series: Lecture Notes in Computer Science ((TOPNOC,volume 9930))

Abstract

Given a log L, a control-flow discovery algorithm f, and a quality metric m, this paper faces the following problem: what are the parameters in f that mostly influence its application in terms of m when applied to L? This paper proposes a method to face this problem, based on sensitivity analysis, a theory which has been successfully applied in other areas. Clearly, a satisfactory solution to this problem will be crucial to bridge the gap between process discovery algorithms and final users. Additionally, recommendation techniques and meta-techniques like determining the representational bias of an algorithm may benefit from solutions to the problem considered in this paper. The method has been evaluated over a set of logs and two different miners: the inductive miner and the flexible heuristic miner, and the experimental results witness the applicability of the general framework described in this paper.

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Notes

  1. 1.

    This paper is an improved and extended version of [10].

  2. 2.

    \(\mathcal {P}(X)\) denotes the powerset of some set X.

  3. 3.

    Depending on the context, we will consider P either as a parameter list \([p_i,...,p_k]\) or its concrete instantiation \([v_i,...,v_k]\).

  4. 4.

    OAT stands for One (factor) At a Time.

  5. 5.

    A point in the parameter space is the result of assigning specific values to the parameters in the parameter list P: \(p_1\)=\(v_1\),..., \(p_k\)=\(v_k\).

  6. 6.

    The Node Arc Degree measure consists of the average of incoming and outgoing arcs of every node of the process model.

  7. 7.

    For computing \(EE_i\), \(\alpha _i - \beta _i\) is considered to be 1 when the parameter is changed from a disabled to an enabled state, or the other way around (e.g., the last parameter in Table 4).

  8. 8.

    All possible parameter settings the control-flow algorithm allows.

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Acknowledgments

This work as been partially supported by funds from the Spanish Ministry for Economy and Competitiveness (MINECO) and the European Union (FEDER funds) under grant COMMAS (ref. TIN2013-46181-C2-1-R).

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Authors and Affiliations

  1. Universitat Politècnica de Catalunya, Barcelona, Spain

    Joel Ribeiro & Josep Carmona

Authors
  1. Joel Ribeiro
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  2. Josep Carmona
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Correspondence to Joel Ribeiro .

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Editors and Affiliations

  1. Newcastle University , Newcastle upon Tyne, United Kingdom

    Maciej Koutny

  2. FernUniversität in Hagen , Hagen, Germany

    Jörg Desel

  3. Leiden University , Leiden, The Netherlands

    Jetty Kleijn

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Ribeiro, J., Carmona, J. (2016). A Method for Assessing Parameter Impact on Control-Flow Discovery Algorithms. In: Koutny, M., Desel, J., Kleijn, J. (eds) Transactions on Petri Nets and Other Models of Concurrency XI. Lecture Notes in Computer Science(), vol 9930. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-53401-4_9

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  • DOI: https://doi.org/10.1007/978-3-662-53401-4_9

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