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https://hdl.handle.net/1822/52876
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Campo DC | Valor | Idioma |
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dc.contributor.author | Coelho, Fabio | por |
dc.contributor.author | Pereira, José | por |
dc.contributor.author | Vilaça, Ricardo Manuel Pereira | por |
dc.contributor.author | Oliveira, Rui Carlos Mendes de | por |
dc.date.accessioned | 2018-03-19T21:22:52Z | - |
dc.date.issued | 2016 | - |
dc.identifier.isbn | 9783319395760 | - |
dc.identifier.issn | 0302-9743 | - |
dc.identifier.uri | https://hdl.handle.net/1822/52876 | - |
dc.description.abstract | Window functions are a sub-class of analytical operators that allow data to be handled in a derived view of a given relation, while taking into account their neighboring tuples. Currently, systems bypass parallelization opportunities which become especially relevant when considering Big Data as data is naturally partitioned. We present a shuffling technique to improve the parallel execution of window functions when data is naturally partitioned when the query holds a partitioning clause that does not match the natural partitioning of the relation. We evaluated this technique with a non-cumulative ranking function and we were able to reduce data transfer among parallel workers in 85% when compared to a naive approach. | por |
dc.description.sponsorship | This work was part-funded by project LeanBigData: UltraScalable and Ultra-Efficient Integrated and Visual Big Data Analytics (FP7-619606), and by the ERDF – European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 Programme within project ≪POCI-01-0145-FEDER-006961≫, and by National Funds through the FCT – Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) as part of project UID/EEA/50014/2013. | por |
dc.language.iso | eng | por |
dc.publisher | Springer International Publishing AG | por |
dc.relation | info:eu-repo/grantAgreement/EC/FP7/619606/EU | por |
dc.relation | info:eu-repo/grantAgreement/FCT/5876/147326/PT | por |
dc.rights | restrictedAccess | por |
dc.title | Holistic shuffler for the parallel processing of SQL window functions | por |
dc.type | conferencePaper | por |
dc.peerreviewed | yes | por |
oaire.citationStartPage | 75 | por |
oaire.citationEndPage | 81 | por |
oaire.citationVolume | 9687 | por |
dc.date.updated | 2018-03-16T13:37:30Z | - |
dc.identifier.doi | 10.1007/978-3-319-39577-7_6 | por |
dc.description.publicationversion | info:eu-repo/semantics/publishedVersion | por |
dc.subject.wos | Science & Technology | - |
sdum.export.identifier | 4559 | - |
sdum.journal | Lecture Notes in Computer Science | por |
sdum.conferencePublication | DISTRIBUTED APPLICATIONS AND INTEROPERABLE SYSTEMS, DAIS 2016 | por |
sdum.bookTitle | DISTRIBUTED APPLICATIONS AND INTEROPERABLE SYSTEMS, DAIS 2016 | por |
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holistic-proceedings.pdf Acesso restrito! | 274,21 kB | Adobe PDF | Ver/Abrir |