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A NLP-Oriented Methodology to Enhance Event Log Quality

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Enterprise, Business-Process and Information Systems Modeling (BPMDS 2021, EMMSAD 2021)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 421))

Abstract

The quality of event logs is a crucial cornerstone for the feasibility of the application of later process mining techniques. The wide variety of data that can be included in an event log refer to information about the activity, such as what, who or where. In this paper, we focus on event logs that include textual information written in a natural language that contains exhaustive descriptions of activity executions. In this context, a pre-processing step is necessary since textual information is unstructured and it can contain inaccuracies that will provoke the impracticability of process mining techniques. For this reason, we propose a methodology that applies Natural Language Processing (NLP) to raw event log by relabelling activities. The approach let the customised description of the measurement and assessment of the event log quality depending on expert requirements. Additionally, it guides the selection of the most suitable NLP techniques for use depending on the event log. The methodology has been evaluated using a real-life event log that includes detailed textual descriptions to capture the management of incidents in the aircraft assembly process in aerospace manufacturing.

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Notes

  1. 1.

    We use l() function to define the length of a label description.

  2. 2.

    All the tag sets used in this work come from the community open project called Universal Dependencies (https://universaldependencies.org/).

  3. 3.

    Characteristics of the event log: 11.342 cases, 114.473 events, number of different labels 78.012, and 10.811 variants.

  4. 4.

    https://github.com/explosion/spacy-models/releases//tag/es_core_news_lg-3.0.0.

  5. 5.

    Language-Tool: https://github.com/languagetool-org/languagetool.

  6. 6.

    http://www.idea.us.es/loading-nlp.

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Acknowledgement

Projects (RTI2018-094283-B-C33, RTI2018-098062-A-I00), funded by: FEDER/Ministry of Science and Innovation - State Research, and the Junta de Andalucía via the COPERNICA (P20_01224) project.

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Authors and Affiliations

  1. IDEA & ITALICA Research Groups, Universidad de Sevilla, Seville, Spain

    Belén Ramos-Gutiérrez, Ángel Jesús Varela-Vaca, F. Javier Ortega & María Teresa Gómez-López

  2. Queensland University of Technology (QUT), Brisbane, Australia

    Moe Thandar Wynn

Authors
  1. Belén Ramos-Gutiérrez
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  2. Ángel Jesús Varela-Vaca
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  3. F. Javier Ortega
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  4. María Teresa Gómez-López
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  5. Moe Thandar Wynn
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Corresponding author

Correspondence to Belén Ramos-Gutiérrez .

Editor information

Editors and Affiliations

  1. University of Melbourne, Parkville, VIC, Australia

    Adriano Augusto

  2. University of Technology Sydney, Ultimo, NSW, Australia

    Asif Gill

  3. University Paris 1 Panthéon-Sorbonne, Paris, France

    Selmin Nurcan

  4. University of Haifa, Haifa, Israel

    Iris Reinhartz-Berger

  5. Munich University of Applied Sciences, Munich, Germany

    Rainer Schmidt

  6. Stockholm University, Kista, Sweden

    Jelena Zdravkovic

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Ramos-Gutiérrez, B., Varela-Vaca, Á.J., Ortega, F.J., Gómez-López, M.T., Wynn, M.T. (2021). A NLP-Oriented Methodology to Enhance Event Log Quality. In: Augusto, A., Gill, A., Nurcan, S., Reinhartz-Berger, I., Schmidt, R., Zdravkovic, J. (eds) Enterprise, Business-Process and Information Systems Modeling. BPMDS EMMSAD 2021 2021. Lecture Notes in Business Information Processing, vol 421. Springer, Cham. https://doi.org/10.1007/978-3-030-79186-5_2

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  • DOI: https://doi.org/10.1007/978-3-030-79186-5_2

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