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Enhancing Event Log Quality : Detecting and Quantifying Timestamp Imperfections

Title data

Fischer, Dominik Andreas ; Goel, Kanika ; Andrews, Robert ; van Dun, Christopher ; Wynn, Moe ; Röglinger, Maximilian:
Enhancing Event Log Quality : Detecting and Quantifying Timestamp Imperfections.
2020
Event: 18th International Conference on Business Process Management (BPM) , Seville.
(Conference item: Conference , Paper )

Official URL: Volltext

Project information

Project title:
Project's official titleProject's id
Projektgruppe WI Wertorientiertes ProzessmanagementNo information

Abstract in another language

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 all timestamps in an event log should be evaluated thoroughly before the event log is used as input for any Process Mining activity. To the best of our knowledge, research that focuses 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 propose to assess the quality of timestamps in an event log across two axes: four levels of abstraction (event, activity, trace, log) and four quality dimensions (accuracy, completeness, consistency, uniqueness). In total, we define 15 timestamp quality-related metrics and demonstrate how they can be computed to assess 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. This approach paves the way for a systematic and interactive enhancement of timestamp imperfections during the data pre-processing phase of Process Mining projects.

Further data

Item Type: Conference item (Paper)
Refereed: Yes
Keywords: Process Mining; Event log; Data quality; Timestamps; Data quality assessment
Institutions of the University: Faculties > Faculty of Law, Business and Economics > Department of Business Administration
Faculties > Faculty of Law, Business and Economics > Department of Business Administration > Chair Information Systems and Value-Based Business Process Management
Faculties > Faculty of Law, Business and Economics > Department of Business Administration > Chair Information Systems and Value-Based Business Process Management > Chair Information Systems and Value-Based Business Process Management - Univ.-Prof. Dr. Maximilian Röglinger
Research Institutions
Research Institutions > Affiliated Institutes
Research Institutions > Affiliated Institutes > Fraunhofer Project Group Business and Information Systems Engineering
Research Institutions > Affiliated Institutes > FIM Research Center Finance & Information Management
Result of work at the UBT: Yes
DDC Subjects: 000 Computer Science, information, general works > 004 Computer science
300 Social sciences > 330 Economics
Date Deposited: 26 Jun 2020 09:05
Last Modified: 26 Jun 2020 09:05
URI: https://eref.uni-bayreuth.de/id/eprint/55618