Article

Enhancing Event Log Quality: Detecting and Quantifying Timestamp Imperfections

Authors:

Dominik Andreas Fischer,

Robert Andrews,

Christopher Gerhard Johannes van Dun,

Moe Thandar Wynn,

Maximilian RöglingerAuthors Info & Claims

Business Process Management: 18th International Conference, BPM 2020, Seville, Spain, September 13–18, 2020, Proceedings

Pages 309 - 326

https://doi.org/10.1007/978-3-030-58666-9_18

Published: 13 September 2020 Publication History

Abstract

Timestamp information recorded in event logs plays a crucial role in uncovering meaningful insights into business process performance and behaviour via Process Mining techniques. Inaccurate or incomplete timestamps may cause activities in a business process to be ordered incorrectly, leading to unrepresentative process models and incorrect process performance analysis results. Thus, the quality of timestamps in an event log should be evaluated thoroughly before the log is used as input for any Process Mining activity. To the best of our knowledge, research on the (automated) quality assessment of event logs remains scarce. Our work presents an automated approach for detecting and quantifying timestamp-related issues (timestamp imperfections) in an event log. We define 15 metrics related to timestamp quality across two axes: four levels of abstraction (event, activity, trace, log) and four quality dimensions (accuracy, completeness, consistency, uniqueness). We adopted the design science research paradigm and drew from knowledge related to data quality as well as event log quality. The approach has been implemented as a prototype within the open-source Process Mining framework ProM and evaluated using three real-life event logs and involving experts from practice. This approach paves the way for a systematic and interactive enhancement of timestamp imperfections during the data pre-processing phase of Process Mining projects.

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Cited By

Kapugama Geeganage DWynn Mter Hofstede A(2024)Text2EL+: Expert Guided Event Log Enrichment Using Unstructured TextJournal of Data and Information Quality10.1145/364001816:1(1-28)Online publication date: 6-Mar-2024
https://dl.acm.org/doi/10.1145/3640018
Goel KLeemans SMartin NWynn M(2022)Quality-Informed Process Mining: A Case for Standardised Data Quality AnnotationsACM Transactions on Knowledge Discovery from Data10.1145/351170716:5(1-47)Online publication date: 5-Apr-2022
https://dl.acm.org/doi/10.1145/3511707
van Hulzen GMartin NDepaire BSouverijns G(2022)Supporting capacity management decisions in healthcare using data-driven process simulationJournal of Biomedical Informatics10.1016/j.jbi.2022.104060129:COnline publication date: 1-May-2022
https://dl.acm.org/doi/10.1016/j.jbi.2022.104060
Show More Cited By

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Published In

cover image Guide Proceedings

Business Process Management: 18th International Conference, BPM 2020, Seville, Spain, September 13–18, 2020, Proceedings

Sep 2020

556 pages

ISBN:978-3-030-58665-2

DOI:10.1007/978-3-030-58666-9

Editors:
Dirk Fahland
Eindhoven University of Technology, Eindhoven, The Netherlands
,
Chiara Ghidini
FBK-irst, Trento, Italy
,
Jörg Becker
University of Münster, Münster, Germany
,
Marlon Dumas
University of Tartu, Tartu, Estonia

© Springer Nature Switzerland AG 2020.

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 13 September 2020

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Cited By

Kapugama Geeganage DWynn Mter Hofstede A(2024)Text2EL+: Expert Guided Event Log Enrichment Using Unstructured TextJournal of Data and Information Quality10.1145/364001816:1(1-28)Online publication date: 6-Mar-2024
https://dl.acm.org/doi/10.1145/3640018
Goel KLeemans SMartin NWynn M(2022)Quality-Informed Process Mining: A Case for Standardised Data Quality AnnotationsACM Transactions on Knowledge Discovery from Data10.1145/351170716:5(1-47)Online publication date: 5-Apr-2022
https://dl.acm.org/doi/10.1145/3511707
van Hulzen GMartin NDepaire BSouverijns G(2022)Supporting capacity management decisions in healthcare using data-driven process simulationJournal of Biomedical Informatics10.1016/j.jbi.2022.104060129:COnline publication date: 1-May-2022
https://dl.acm.org/doi/10.1016/j.jbi.2022.104060
Fischer DGoel KAndrews Rvan Dun CWynn MRöglinger M(2022)Towards interactive event log forensicsInformation Systems10.1016/j.is.2022.102039109:COnline publication date: 1-Nov-2022
https://dl.acm.org/doi/10.1016/j.is.2022.102039

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