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Towards the Detection of Promising Processes by Analysing the Relational Data

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ADBIS, TPDL and EDA 2020 Common Workshops and Doctoral Consortium (TPDL 2020, ADBIS 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1260))

Abstract

Business process discovery provides mechanisms to extract the general process behaviour from event observations. However, not always the logs are available and must be extracted from repositories, such as relational databases. Derived from the references that exist between the relational tables, several are the possible combinations of traces of events that can be extracted from a relational database. Different traces can be extracted depending on which attribute represents the \(case_{-}id\), what are the attributes that represent the execution of an activity, or how to obtain the timestamp to define the order of the events. This paper proposes a method to analyse a wide range of possible traces that could be extracted from a relational database, based on measuring the level of interest of extracting a trace log, later used for a discovery process. The analysis is done by means of a set of proposed metrics before the traces are generated and the process is discovered. This analysis helps to reduce the computational cost of process discovery. For a possible \(case_{-}id\) every possible traces are analysed and measured. To validate our proposal, we have used a real relational database, where the detection of processes (most and least promising) are compared to rely on our proposal.

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Notes

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Acknowledgement

This research was partially supported by Ministry of Science and Technology of Spain with projects ECLIPSE (RTI2018-094283-B-C33) and by Junta de Andalucía with METAMORFOSIS projects; and by European Regional Development Fund (ERDF/FEDER).

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Authors and Affiliations

  1. Universidad de Sevilla, Sevilla, Spain

    Belén Ramos-Gutiérrez & María Teresa Gómez-López

  2. Universidad Loyola Andalucía, Sevilla, Spain

    Luisa Parody

Authors
  1. Belén Ramos-Gutiérrez
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  2. Luisa Parody
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  3. María Teresa Gómez-López
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Corresponding author

Correspondence to Belén Ramos-Gutiérrez .

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Editors and Affiliations

  1. ISAE-ENSMA, Poitiers, France

    Ladjel Bellatreche

  2. Slovak University of Technology, Bratislava, Slovakia

    Mária Bieliková

  3. Université Lumière Lyon 2, Lyon, France

    Omar Boussaïd

  4. University of Genova, Genova, Italy

    Barbara Catania

  5. Université Lumière Lyon 2, Lyon, France

    Jérôme Darmont

  6. Leibniz University of Hannover, Hannover, Niedersachsen, Germany

    Elena Demidova

  7. Université Claude Bernard Lyon 1, Lyon, France

    Fabien Duchateau

  8. The Open University, Milton Keynes, UK

    Mark Hall

  9. University of Ljubljana, Ljubljana, Slovenia

    Tanja Merčun

  10. National Research University Higher School of Economics, St. Petersburg, Russia

    Boris Novikov

  11. Ionian University, Corfu, Greece

    Christos Papatheodorou

  12. Goethe University Frankfurt, Frankfurt am Main, Hessen, Germany

    Thomas Risse

  13. Universitat Politècnica de Catalunya, Barcelona, Spain

    Oscar Romero

  14. AgroParisTech, Montpellier, France

    Lucile Sautot

  15. University of Lyon, Lyon, France

    Guilaine Talens

  16. Poznań University of Technology, Poznań, Poland

    Robert Wrembel

  17. University of Ljubljana, Ljubljana, Slovenia

    Maja Žumer

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Ramos-Gutiérrez, B., Parody, L., Gómez-López, M.T. (2020). Towards the Detection of Promising Processes by Analysing the Relational Data. In: Bellatreche, L., et al. ADBIS, TPDL and EDA 2020 Common Workshops and Doctoral Consortium. TPDL ADBIS 2020 2020. Communications in Computer and Information Science, vol 1260. Springer, Cham. https://doi.org/10.1007/978-3-030-55814-7_24

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  • DOI: https://doi.org/10.1007/978-3-030-55814-7_24

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  • Online ISBN: 978-3-030-55814-7

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