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dc.creatorde Haro Olmo, Francisco Josées
dc.creatorValencia Parra, Álvaroes
dc.creatorVarela Vaca, Ángel Jesúses
dc.creatorÁlvarez Bermejo, José Antonioes
dc.creatorGómez López, María Teresaes
dc.date.accessioned2023-10-02T10:36:58Z
dc.date.available2023-10-02T10:36:58Z
dc.date.issued2023
dc.identifier.citationde Haro Olmo, F.J., Valencia Parra, Á., Varela Vaca, Á.J., Álvarez Bermejo, J.A. y Gómez López, M.T. (2023). ELI: an IoT-aware big data pipeline with data curation and data quality. Peer J Computer Science, 9:e1605, 1-24. https://doi.org/10.7717/peerj-cs.1605.
dc.identifier.issn2376-5992es
dc.identifier.urihttps://hdl.handle.net/11441/149268
dc.description.abstractThe complexity of analysing data from IoT sensors requires the use of Big Data technologies, posing challenges such as data curation and data quality assessment. Not facing both aspects potentially can lead to erroneous decision-making (i.e., processing incorrectly treated data, introducing errors into processes, causing damage or increasing costs). This article presents ELI, an IoT-based Big Data pipeline for developing a data curation process and assessing the usability of data collected by IoT sensors in both offline and online scenarios. We propose the use of a pipeline that integrates data transformation and integration tools and a customisable decision model based on the Decision Model and Notation (DMN) to evaluate the data quality. Our study emphasises the importance of data curation and quality to integrate IoT information by identifying and discarding low-quality data that obstruct meaningful insights and introduce errors in decision making. We evaluated our approach in a smart farm scenario using agricultural humidity and temperature data collected from various types of sensors. Moreover, the proposed model exhibited consistent results in offline and online (stream data) scenarios. In addition, a performance evaluation has been developed, demonstrating its effectiveness. In summary, this article contributes to the development of a usable and effective IoT-based Big Data pipeline with data curation capabilities and assessing data usability in both online and offline scenarios. Additionally, it introduces customisable decision models for measuring data quality across multiple dimensions.es
dc.description.sponsorshipMinisterio de Ciencia e Innovación (MICIN) España AEI/10.13039/501100011033es
dc.formatapplication/pdfes
dc.format.extent24 p.es
dc.language.isoenges
dc.publisherPeerJes
dc.relation.ispartofPeer J Computer Science, 9:e1605, 1-24.
dc.rightsAtribución 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectData curationes
dc.subjectData qualityes
dc.subjectBig data pipelinees
dc.subjectInternet of Thingses
dc.subjectSensorses
dc.titleELI: an IoT-aware big data pipeline with data curation and data qualityes
dc.typeinfo:eu-repo/semantics/articlees
dcterms.identifierhttps://ror.org/03yxnpp24
dc.type.versioninfo:eu-repo/semantics/publishedVersiones
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.contributor.affiliationUniversidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticoses
dc.relation.projectIDAEI/10.13039/501100011033es
dc.relation.projectIDUS-1381375es
dc.relation.projectIDP20_01224es
dc.relation.publisherversionhttps://peerj.com/articles/cs-1605/es
dc.identifier.doi10.7717/peerj-cs.1605es
dc.journaltitlePeer J Computer Sciencees
dc.publication.volumen9:e1605es
dc.publication.initialPage1es
dc.publication.endPage24es

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