Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/36500
Title: DaQAPO: Supporting flexible and fine-grained event log quality assessment
Authors: MARTIN, Niels 
VAN HOUDT, Greg 
JANSSENSWILLEN, Gert 
Issue Date: 2022
Publisher: 
Source: Expert systems with applications, 191 (Art N° 116274)
Abstract: Process mining can provide valuable insights in business processes using an event log containing process execution data. Despite the significant potential of process mining to support the analysis and improvement of processes, the reliability of process mining outcomes depends on the quality of the event log. Real-life logs typically suffer from various data quality issues. Consequently, thorough event log quality assessment is required before applying process mining algorithms. This paper introduces DaQAPO, the first R-package which supports flexible and fine-grained event log quality assessment. It provides a rich set of tests to identify a wide range of event log quality issues, while having sufficient flexibility to allow the detection of context-specific quality issues.
Keywords: Process mining;Event log quality assessment;Event log quality;Data quality;Event log;R
Document URI: http://hdl.handle.net/1942/36500
ISSN: 0957-4174
e-ISSN: 1873-6793
DOI: 10.1016/j.eswa.2021.116274
ISI #: 000744171700001
Category: A1
Type: Journal Contribution
Appears in Collections:Research publications

Files in This Item:
File Description SizeFormat 
1-s2.0-S0957417421015827-main.pdf
  Restricted Access
Published version1.86 MBAdobe PDFView/Open    Request a copy
Manuscript - Accepted version document server.pdf
  Until 2024-04-01
Peer-reviewed author version423.43 kBAdobe PDFView/Open    Request a copy
Show full item record

WEB OF SCIENCETM
Citations

1
checked on Nov 27, 2022

Page view(s)

58
checked on Aug 30, 2022

Download(s)

56
checked on Aug 30, 2022

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.