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

Hiroki Horita, Yuta Kurihashi and Nozomi Miyamori
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Hiroki Horita: Graduate School of Science and Engineering, Ibaraki University, Ibaraki 310-8512, Japan
Yuta Kurihashi: Graduate School of Science and Engineering, Ibaraki University, Ibaraki 310-8512, Japan
Nozomi Miyamori: Graduate School of Science and Engineering, Ibaraki University, Ibaraki 310-8512, Japan

Data, 2020, vol. 5, issue 3, 1-12

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.

Keywords: process mining; business process management; data quality; data management (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2020
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