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Revealing the Importance of Setting Parameters in Declarative Discovery Algorithms: An Evolutionary-Based Methodology

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Advanced Information Systems Engineering (CAiSE 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13901))

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Abstract

Through constraints, declarative process models represent the permitted behaviour associated with a business process, by limiting the potential correct traces. These models can be discovered by analysing an event log. However, various declarative business models can be extracted from a single event log, depending on the desirable level of metrics, such as fitness and generalisation. Existing discovery algorithms enable the type of discovered declarative process model to be customised through a set of configuration parameters. Depending on the values of these parameters, the discovered process can be of high or low quality. Unfortunately, the high number of combinatorial parameters and the high time consumption of process discovery make it impractical to conduct an exhaustive analysis of the configuration parameters to determine the most suitable declarative process model discovered. As a solution, we propose a methodology supported by an implemented framework that uses evolutionary algorithms to reduce computational complexity and to select the highest quality declarative business processes. An experiment is included to show the feasibility of our proposal.

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Notes

  1. 1.

    Conditions allow transitions to occur only under specific circumstances.

  2. 2.

    The value of \(\text {AHD}\) lies within the range of 0 to 5, whereby a lower value indicates a better result.

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Acknowledgement

This work has been funded by the following projects: AETHER-US (PID2020-112540RB-C44), ALBA-US (TED2021-130355B-C32) AEI/10.13039/501100011033/Unión Europea NextGenerationEU/PRTR, METAMORFOSIS (US-1381375), and COPERNICA (P20-01224).

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Authors and Affiliations

  1. University of Seville, IDEA Research Group, Seville, Spain

    Vanessa Pradas Fernández, Ángel Jesús Varela-Vaca & María Teresa Gómez-López

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  1. Vanessa Pradas Fernández
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  2. Ángel Jesús Varela-Vaca
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  3. María Teresa Gómez-López
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Correspondence to María Teresa Gómez-López .

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Editors and Affiliations

  1. The University of Queensland, Brisbane, QLD, Australia

    Marta Indulska

  2. University of Haifa, Haifa, Israel

    Iris Reinhartz-Berger

  3. Universidad San Jorge, Zaragoza, Spain

    Carlos Cetina

  4. Universitat Politècnica de València, Valencia, Spain

    Oscar Pastor

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Fernández, V.P., Varela-Vaca, Á.J., Gómez-López, M.T. (2023). Revealing the Importance of Setting Parameters in Declarative Discovery Algorithms: An Evolutionary-Based Methodology. In: Indulska, M., Reinhartz-Berger, I., Cetina, C., Pastor, O. (eds) Advanced Information Systems Engineering. CAiSE 2023. Lecture Notes in Computer Science, vol 13901. Springer, Cham. https://doi.org/10.1007/978-3-031-34560-9_10

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  • DOI: https://doi.org/10.1007/978-3-031-34560-9_10

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