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Extraction of Missing Tendency Using Decision Tree Learning in Business Process Event Log

Author

Listed:
  • Hiroki Horita

    (Graduate School of Science and Engineering, Ibaraki University, Ibaraki 310-8512, Japan
    Current address: Hitachi, Ibaraki 316-8511, Japan.)

  • Yuta Kurihashi

    (Graduate School of Science and Engineering, Ibaraki University, Ibaraki 310-8512, Japan
    Current address: Hitachi, Ibaraki 316-8511, Japan.)

  • Nozomi Miyamori

    (Graduate School of Science and Engineering, Ibaraki University, Ibaraki 310-8512, Japan
    Current address: Hitachi, Ibaraki 316-8511, Japan.)

Abstract

In recent years, process mining has been attracting attention as an effective method for improving business operations by analyzing event logs that record what is done in business processes. The event log may contain missing data due to technical or human error, and if the data are missing, the analysis results will be inadequate. Traditional methods mainly use prediction completion when there are missing values, but accurate completion is not always possible. In this paper, we propose a method for understanding the tendency of missing values in the event log using decision tree learning without supplementing the missing values. We conducted experiments using data from the incident management system and confirmed the effectiveness of our method.

Suggested Citation

  • Hiroki Horita & Yuta Kurihashi & Nozomi Miyamori, 2020. "Extraction of Missing Tendency Using Decision Tree Learning in Business Process Event Log," Data, MDPI, vol. 5(3), pages 1-12, September.
    • Handle: RePEc:gam:jdataj:v:5:y:2020:i:3:p:82-:d:411210
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    References listed on IDEAS

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    1. Robert Andrews & Moe T. Wynn & Kirsten Vallmuur & Arthur H. M. ter Hofstede & Emma Bosley & Mark Elcock & Stephen Rashford, 2019. "Leveraging Data Quality to Better Prepare for Process Mining: An Approach Illustrated Through Analysing Road Trauma Pre-Hospital Retrieval and Transport Processes in Queensland," IJERPH, MDPI, vol. 16(7), pages 1-25, March.
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