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Event Log Reconstruction Using Autoencoders

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Service-Oriented Computing – ICSOC 2018 Workshops (ICSOC 2018)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 11434))

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Abstract

Poor quality of process event logs prevents high quality business process analysis and improvement. Process event logs quality decreases because of missing attribute values or after incorrect or irrelevant attribute values are identified and removed. Reconstructing a correct value for these missing attributes is likely to increase the quality of event log-based process analyses. Traditional statistical reconstruction methods work poorly with event logs, because of the complex interrelations among attributes, events and cases. Machine learning approaches appear more suitable in this context, since they can learn complex models of event logs through training. This paper proposes a method for reconstructing missing attribute values in event logs based on the use of autoencoders. Autoencoders are a class of feed-forward neural networks that reconstruct their own input after having learnt a model of its latent distribution. They suit problems of unsupervised learning, such as the one considered in this paper. When reconstructing missing attribute values in an event log, in fact, one cannot assume that a training set with true labels is available for model training. The proposed method is evaluated on two real event logs against baseline methods commonly used in the literature for imputing missing values in large datasets.

This work received fundings from NRF Korea Project Number 2017076589.

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Notes

  1. 1.

    at: https://data.4tu.nl/repository/collection:event_logs_real.

  2. 2.

    https://github.com/pytorch.

  3. 3.

    https://github.com/hoangnguyen3892/event-log-reconstruction.

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Authors and Affiliations

  1. Trusting Social, Ho Chi Minh City, Vietnam

    Hoang Thi Cam Nguyen

  2. Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea

    Marco Comuzzi

Authors
  1. Hoang Thi Cam Nguyen
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  2. Marco Comuzzi
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Corresponding author

Correspondence to Marco Comuzzi .

Editor information

Editors and Affiliations

  1. Deakin University, Melbourne, VIC, Australia

    Xiao Liu

  2. University of Pau and Pays, Pau Cedex, France

    Michael Mrissa

  3. Fudan University, Shanghai Shi, China

    Liang Zhang

  4. LIRIS Lab, University Lyon 1, IUT, Villeurbanne Cedex, France

    Djamal Benslimane

  5. School of IT and Computer Science, University of Wollongong, Wollongong, NSW, Australia

    Aditya Ghose

  6. Harbin Institute of Technology, Harbin, China

    Zhongjie Wang

  7. Scientific and Technological Hub, Fondazione Bruno Kessler (FBK), Trento, Italy

    Antonio Bucchiarone

  8. Macquarie University, Sydney, NSW, Australia

    Wei Zhang

  9. Queen’s University, Kingston, ON, Canada

    Ying Zou

  10. Rochester Institute of Technology, Rochester, NY, USA

    Qi Yu

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Nguyen, H.T.C., Comuzzi, M. (2019). Event Log Reconstruction Using Autoencoders. In: Liu, X., et al. Service-Oriented Computing – ICSOC 2018 Workshops. ICSOC 2018. Lecture Notes in Computer Science(), vol 11434. Springer, Cham. https://doi.org/10.1007/978-3-030-17642-6_28

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  • DOI: https://doi.org/10.1007/978-3-030-17642-6_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-17641-9

  • Online ISBN: 978-3-030-17642-6

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