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However, most of the existing algorithms in process mining, a field focusing on data-driven process analysis, do not take any data quality issues or the potential effects of data pre-processing into account explicitly. This can result in erroneous process mining results, leading to inaccurate, or misleading conclusions about the process under investigation. To address this gap, we propose data quality annotations for event logs, which can be used by process mining algorithms to generate quality-informed insights. Using a design science approach, requirements are formulated, which are leveraged to propose data quality annotations. Moreover, we present the \u201cQuality-Informed visual Miner\u201d plug-in to demonstrate the potential utility and impact of data quality annotations. Our experimental results, utilising both synthetic and real-life event logs, show how the use of data quality annotations by process mining techniques can assist in increasing the reliability of performance analysis results.<\/jats:p>","DOI":"10.1145\/3511707","type":"journal-article","created":{"date-parts":[[2022,2,4]],"date-time":"2022-02-04T21:51:01Z","timestamp":1644011461000},"page":"1-47","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":16,"title":["Quality-Informed Process Mining: A Case for Standardised Data Quality Annotations"],"prefix":"10.1145","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6250-2589","authenticated-orcid":false,"given":"Kanika","family":"Goel","sequence":"first","affiliation":[{"name":"School of Information Systems, Queensland University of Technology, Brisbane, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5201-7125","authenticated-orcid":false,"given":"Sander J. 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