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dc.contributor.authorGómez Brandón, Adrián
dc.contributor.authorParamá Gabia, José Ramón
dc.contributor.authorVillalobos Rodríguez, Kevin
dc.contributor.authorIllarramendi Echave, María Aránzazu ORCID
dc.contributor.authorRodríguez Brisaboa, Nieves
dc.date.accessioned2021-12-09T09:18:28Z
dc.date.available2021-12-09T09:18:28Z
dc.date.issued2021-11
dc.identifier.citationComputers in Industry 132 : (2021) // Article ID 103503es_ES
dc.identifier.issn0166-3615
dc.identifier.issn1872-6194
dc.identifier.urihttp://hdl.handle.net/10810/54392
dc.description.abstract[EN]The new opportunities generated by the data-driven economy in the manufacturing industry have caused many companies opt for it. However, the size of time series data that need to be captured creates the problem of having to assume high storage costs. Moreover, these costs, which are constantly growing, begin to have an impact on the profitability of companies. Thus, in this scenario, the need arises to develop techniques that allow obtaining reduced representations of the time series. In this paper, we present a lossless compression method for industrial time series that allows an efficient access. That is, our aim goes beyond pure compression, where the usual way to access the data requires a complete decompression of the dataset before processing it. Instead, our method allows decompressing portions of the dataset, and moreover, it allows direct querying the compressed data. Thus, the proposed method combines the efficient access, typical of lossy methods, with the lossless compression.es_ES
dc.description.sponsorshipFor the A Coruna team: This work was supported by CITIC, as Research Center accredited by Galician University System, is funded by "Conselleria de Cultura, Educacion e Universidade from Xunta de Galicia", supported in an 80% through ERDF Funds, ERDF Operational Programme Galicia 2014-2020, and the remaining 20% by "Secretaria Xeral de Universidades" (Grant ED431G 2019/01) , Xunta de Galicia/FEDER-UE under Grants [IG240.2020.1.185; IN852A 2018/14] and Ministerio de Ciencia, Innovacion under Grants [TIN2016-78011-C4-1-R; RTC-2017-5908-7] . For the Basque team: Ministerio de Ciencia, Innovacion y Universidades under Grant [FEDER/TIN2016-78011-C4-2-R] and the Basque Government under Grant No. [IT1330-19] . Funding for open access charge: Universidade da Coruna/CISUG.es_ES
dc.language.isoenges_ES
dc.publisherElsevieres_ES
dc.relationinfo:eu-repo/grantAgreement/MICINN/TIN2016-78011-C4-1-R;es_ES
dc.relationinfo:eu-repo/grantAgreement/MICINN/RTC-2017-5908-7es_ES
dc.relationinfo:eu-repo/grantAgreement/MICINN/TIN2016-78011-C4-2-Res_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subjectcompressiones_ES
dc.subjectsmart manufacturinges_ES
dc.subjecttime serieses_ES
dc.titleLossless compression of industrial time series with direct accesses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.holder(c) 2021 The Author(s). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).es_ES
dc.rights.holderAtribución-NoComercial-SinDerivadas 3.0 España*
dc.relation.publisherversionhttps://www-sciencedirect-com.ehu.idm.oclc.org/science/article/pii/S016636152100110X?via%3Dihubes_ES
dc.identifier.doi10.1016/j.compind.2021.103503
dc.departamentoesLenguajes y sistemas informáticoses_ES
dc.departamentoeuHizkuntza eta sistema informatikoakes_ES


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(c) 2021 The Author(s). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Except where otherwise noted, this item's license is described as (c) 2021 The Author(s). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).