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Analysis of Language Inspired Trace Representation for Anomaly Detection

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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

A great concern for organizations is to detect anomalous process instances within their business processes. For that, conformance checking performs model-aware analysis by comparing process logs to business models for the detection of anomalous process executions. However, in several scenarios, a model is either unavailable or its generation is costly, which requires the employment of alternative methods to allow a confident representation of traces. This work supports the analysis of language inspired process analysis grounded in the word2vec encoding algorithm. We argue that natural language encodings correctly model the behavior of business processes, supporting a proper distinction between common and anomalous behavior. In the experiments, we compared accuracy and time cost among different word2vec setups and classic encoding methods (token-based replay and alignment features), addressing seven different anomaly scenarios. Feature importance values and the impact of different anomalies in seven event logs were also evaluated to bring insights on the trace representation subject. Results show the proposed encoding overcomes representational capability of traditional conformance metrics for the anomaly detection task.

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Notes

  1. 1.

    https://pm4py.fit.fraunhofer.de/documentation.

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Authors and Affiliations

  1. Università degli Studi di Milano (UNIMI), Milan, Italy

    Gabriel Marques Tavares

  2. Londrina State University (UEL), Londrina, Brazil

    Sylvio Barbon Jr.

Authors
  1. Gabriel Marques Tavares
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  2. Sylvio Barbon Jr.
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Corresponding author

Correspondence to Gabriel Marques Tavares .

Editor information

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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Tavares, G.M., Barbon, S. (2020). Analysis of Language Inspired Trace Representation for Anomaly Detection. 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_25

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  • DOI: https://doi.org/10.1007/978-3-030-55814-7_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-55813-0

  • Online ISBN: 978-3-030-55814-7

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