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Data Quality Assessment: A Practical Application

datacite.subject.fosCiências Naturais::Matemáticas
datacite.subject.sdg09:Indústria, Inovação e Infraestruturas
dc.contributor.authorEliana Costa e Silva
dc.contributor.authorTeresa Peixoto
dc.contributor.authorÓscar Oliveira
dc.contributor.authorBruno Oliveira
dc.date.accessioned2025-07-10T14:10:15Z
dc.date.available2025-07-10T14:10:15Z
dc.date.issued2025-06
dc.description.abstractThis paper presents a novel data quality score designed to address the challenges of ensuring high-quality data in Internet of Things (IoT) deployments. Given the growing reliance on IoT systems and the volume of data they generate, maintaining data quality is essential for reliable decision-making and effective analytics. The proposed score synthesizes key data quality dimensions, providing a comprehensive measure of data quality that can be applied across various IoT contexts. The results obtained for a public dataset on a water pumping system show the applicability and flexibility of the proposed data quality score. This work contributes to the ongoing efforts to improve data management in IoT environments, ultimately supporting the development of robust, data-driven solutions.eng
dc.identifier.doihttps://doi.org/10.1007/978-3-031-94484-0_42
dc.identifier.urihttp://hdl.handle.net/10400.22/30219
dc.language.isoeng
dc.peerreviewedyes
dc.publisherSpringer
dc.relationUIDB/04728/2020
dc.relationUIDP/04728/2020
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.titleData Quality Assessment: A Practical Applicationpor
dc.typeconference proceedings
dspace.entity.typePublication
oaire.citation.conferencePlacePrague, Chequia
oaire.citation.titleInnovations in Mechanical Engineering IV
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85

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