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FAIR is not enough -- A Metrics Framework to ensure Data Quality through Data Preparation

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dc.contributor.authorRestat, Valerie
dc.contributor.authorKlettke, Meike
dc.contributor.authorStörl, Uta
dc.contributor.editorKönig-Ries, Birgitta
dc.contributor.editorScherzinger, Stefanie
dc.contributor.editorLehner, Wolfgang
dc.contributor.editorVossen, Gottfried
dc.date.accessioned2023-02-23T14:00:12Z
dc.date.available2023-02-23T14:00:12Z
dc.date.issued2023
dc.description.abstractData-driven systems and machine learning-based decisions are becoming increasingly important and are having an impact on our everyday lives. The prerequisite for this is good data quality, which must be ensured by preprocessing the data. For domain experts, however, the following difficulties arise: On the one hand, they have to choose from a multitude of different tools and algorithms. On the other hand, there is no uniform evaluation method for data quality. For this reason, we present the design of a framework of metrics that allows for a flexible evaluation of data quality and data preparation results.en
dc.identifier.doi10.18420/BTW2023-61
dc.identifier.isbn978-3-88579-725-8
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/40370
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofBTW 2023
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-331
dc.subjectdata quality
dc.subjectmetrics
dc.subjectevaluation
dc.subjectdata preparation
dc.titleFAIR is not enough -- A Metrics Framework to ensure Data Quality through Data Preparationen
dc.typeText/Conference Paper
gi.citation.endPage929
gi.citation.publisherPlaceBonn
gi.citation.startPage917
gi.conference.date06.-10. März 2023
gi.conference.locationDresden, Germany

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