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EasyBDI: Near Real-Time Data Analytics over Heterogeneous Data Sources

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datacite.subject.fosCiências Naturais::Ciências da Computação e da Informação
datacite.subject.sdg08:Trabalho Digno e Crescimento Económico
datacite.subject.sdg09:Indústria, Inovação e Infraestruturas
datacite.subject.sdg10:Reduzir as Desigualdades
dc.contributor.authorSilva, Bruno
dc.contributor.authorMoreira, José
dc.contributor.authorCosta, Rogério Luís de C.
dc.date.accessioned2026-05-07T09:36:31Z
dc.date.available2026-05-07T09:36:31Z
dc.date.issued2021
dc.descriptionLink de acesso ao documento - https://openproceedings.org/2021/conf/edbt/p190.pdf
dc.descriptionConference date - 23 March 2021 - 26 March 2021; Conference code - 171234
dc.description.abstractThe large volume of currently available data creates several opportunities for sciences and industry, especially with the application of data analytics. But also raises challenges that make unfeasible the use of batch-based ETL processes. Indeed, near real-time data analytics is a requirement in several domains as an alternative to traditional data warehouses. In the last years, big data platforms have been developed to enable query execution over distributed data sources. However, they do not deal with subject-oriented analysis, do not provide data distribution transparency, or do not assist with schema mapping and integration. In this demonstration, we present EasyBDI. It's a near real-time big data analytics prototype that enables users to run queries over heterogeneous data sources based on global logical abstractions created by the system and provides some usual concepts of data warehouse systems, like facts and dimensions. We use two motivating scenarios, one based on three years of real data on photovoltaic energy production and consumption, and the other based on the SSB+ benchmark. We will also present implementation challenges, issues, solutions, and insights.eng
dc.description.sponsorshipThis work is partially funded by National Funds through the FCT (Foundation for Science and Technology) in the context of the projects UIDB/04524/2020, UIDB/00127/2020 and POCI-01-0247-FEDER-024541.
dc.identifier.citationSilva, B., Moreira, J. M., & Costa, R. L. D. C. (2021). EasyBDI: Near Real-Time Data Analytics over Heterogeneous Data Sources. In EDBT (pp. 702-705). DOI: https://doi.org/10.5441/002/edbt.2021.88.
dc.identifier.doi10.5441/002/edbt.2021.88
dc.identifier.isbn978-3-89318-084-4
dc.identifier.urihttp://hdl.handle.net/10400.8/16246
dc.language.isoeng
dc.peerreviewedyes
dc.publisherOpen Proceedings
dc.relationResearch Center in Informatics and Communications
dc.relationInstitute of Electronics and Informatics Engineering of Aveiro
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectDistribution transparency
dc.subjectdata analytics
dc.subjectnear real-time data warehousing
dc.titleEasyBDI: Near Real-Time Data Analytics over Heterogeneous Data Sourceseng
dc.typeconference paper
dspace.entity.typePublication
oaire.awardNumberUIDB/04524/2020
oaire.awardNumberUID/CEC/00127/2013
oaire.awardTitleResearch Center in Informatics and Communications
oaire.awardTitleInstitute of Electronics and Informatics Engineering of Aveiro
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04524%2F2020/PT
oaire.awardURIhttp://hdl.handle.net/10400.8/13663
oaire.citation.conferenceDate2021-03
oaire.citation.conferencePlaceVirtual, Online
oaire.citation.endPage705
oaire.citation.startPage702
oaire.citation.titleProceedings of the 24th International Conference on Extending Database Technology (EDBT)
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNamede Carvalho Costa
person.givenNameRogério Luís
person.identifier.ciencia-id7717-9573-0C0F
person.identifier.orcid0000-0003-2306-7585
person.identifier.ridA-7940-2016
person.identifier.scopus-author-id7801604983
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
relation.isAuthorOfPublication5654d934-3fa0-4afb-9b3b-f2736104924c
relation.isAuthorOfPublication.latestForDiscovery5654d934-3fa0-4afb-9b3b-f2736104924c
relation.isProjectOfPublication67435020-fe0d-4b46-be85-59ee3c6138c7
relation.isProjectOfPublicationa81c1f82-40d2-40f8-9bec-058544e9fd17
relation.isProjectOfPublication.latestForDiscovery67435020-fe0d-4b46-be85-59ee3c6138c7

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The large volume of currently available data creates several opportunities for sciences and industry, especially with the application of data analytics. But also raises challenges that make unfeasible the use of batch-based ETL processes. Indeed, near real-time data analytics is a requirement in several domains as an alternative to traditional data warehouses. In the last years, big data platforms have been developed to enable query execution over distributed data sources. However, they do not deal with subject-oriented analysis, do not provide data distribution transparency, or do not assist with schema mapping and integration. In this demonstration, we present EasyBDI. It's a near real-time big data analytics prototype that enables users to run queries over heterogeneous data sources based on global logical abstractions created by the system and provides some usual concepts of data warehouse systems, like facts and dimensions. We use two motivating scenarios, one based on three years of real data on photovoltaic energy production and consumption, and the other based on the SSB+ benchmark. We will also present implementation challenges, issues, solutions, and insights.
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