Event Log Data Quality Issues and Solutions

Process mining is a discipline that analyzes real event data extracted from information systems that support a business process to construct as-is process models and detect performance issues. Process event data are transformed into event logs, where the level of data quality directly impacts the re...

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Bibliographic Details
Published in:Mathematics (Basel) 2023-07, Vol.11 (13), p.2858
Main Authors: Dakic, Dusanka, Stefanovic, Darko, Vuckovic, Teodora, Zizakov, Marina, Stevanov, Branislav
Format: Article
Language:English
Subjects:
Automation
data quality
event log
Food science
incorrect data
Information systems
Literature reviews
Mineral industry
Mining industry
Missing data
Preprocessing
process mining
Systematic review
Taxonomy
trace clustering
Citations: Items that this one cites
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Summary:Process mining is a discipline that analyzes real event data extracted from information systems that support a business process to construct as-is process models and detect performance issues. Process event data are transformed into event logs, where the level of data quality directly impacts the reliability, validity, and usefulness of the derived process insights. The literature offers a taxonomy of preprocessing techniques and papers reporting on solutions for data quality issues in particular scenarios without exploring the relationship between the data quality issues and solutions. This research aims to discover how process mining researchers and practitioners solve certain data quality issues in practice and investigates the nature of the relationship between data quality issues and preprocessing techniques. Therefore, a study was undertaken among prominent process mining researchers and practitioners, gathering information regarding the perceived importance and frequency of data quality issues and solutions and the participants’ recommendations on preprocessing technique selection. The results reveal the most important and frequent data quality issues and preprocessing techniques and the gap between their perceived frequency and importance. Consequently, an overview of how researchers and practitioners solve data quality issues is presented, allowing the development of recommendations.
ISSN:2227-7390
2227-7390
DOI:10.3390/math11132858