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dc.contributor.authorPetcu, Dana
dc.contributor.authorIuhasz, Gabriel
dc.contributor.authorPop, Daniel
dc.contributor.authorTalia, Domenico
dc.contributor.authorCarretero, Jesús
dc.contributor.authorProdan, Radu
dc.contributor.authorFahringer, Thomas
dc.contributor.authorGrasso, Ivan
dc.contributor.authorDoallo Biempica, Ramón
dc.contributor.authorMartín Santamaría, María José
dc.contributor.authorFraguela, Basilio B.
dc.contributor.authorTrobec, Roman
dc.contributor.authorDepolli, Matjaz
dc.contributor.authorAlmeida Rodriguez, Francisco
dc.contributor.authorSande, Francisco de
dc.contributor.authorDa Costa, Georges
dc.contributor.authorPierson, Jean-Marc
dc.contributor.authorAnastasiadis, Stergios
dc.contributor.authorBartzokas, Aristides
dc.contributor.authorLolis, Christos
dc.contributor.authorGonçalves, Pedro
dc.contributor.authorBrito, Fabrice
dc.contributor.authorBrown, Nick
dc.date.accessioned2018-08-07T10:36:50Z
dc.date.available2018-08-07T10:36:50Z
dc.date.issued2016
dc.identifier.citationPetcu, D., Iuhasz, G., Pop, D., Talia, D., Carretero, J., Prodan, R., ... & Fraguela, B. B. (2016). On processing extreme data. Scalable Computing: Practice and Experience, 16(4), pp-467.es_ES
dc.identifier.issn1895-1767
dc.identifier.urihttp://hdl.handle.net/2183/20948
dc.description.abstract[Abstract] Extreme Data is an incarnation of Big Data concept distinguished by the massive amounts of data that must be queried, communicated and analyzed in near real-time by using a very large number of memory or storage elements and exascale computing systems. Immediate examples are the scientific data produced at a rate of hundreds of gigabits-per-second that must be stored, filtered and analyzed, the millions of images per day that must be analyzed in parallel, the one billion of social data posts queried in real-time on an in-memory components database. Traditional disks or commercial storage nowadays cannot handle the extreme scale of such application data. Following the need of improvement of current concepts and technologies, we focus in this paper on the needs of data intensive applications running on systems composed of up to millions of computing elements (exascale systems). We propose in this paper a methodology to advance the state-of-the-art. The starting point is the definition of new programming paradigms, APIs, runtime tools and methodologies for expressing data-intensive tasks on exascale systems. This will pave the way for the exploitation of massive parallelism over a simplified model of the system architecture, thus promoting high performance and efficiency, offering powerful operations and mechanisms for processing extreme data sources at high speed and/or real time.es_ES
dc.language.isoenges_ES
dc.publisherUniversitatea de Vest din Timisoara,West University of Timisoaraes_ES
dc.relation.urihttps://doi.org/10.12694/scpe.v16i4.1134es_ES
dc.subjectExtreme dataes_ES
dc.subjectHPCes_ES
dc.subjectExascale systemses_ES
dc.subjectExtreme computinges_ES
dc.subjectParallel programming modelses_ES
dc.subjectScalable data analysises_ES
dc.titleOn processing extreme dataes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessinfo:eu-repo/semantics/openAccesses_ES
UDC.journalTitleScalable Computing. Practice and Experiencees_ES
UDC.volume16es_ES
UDC.issue4es_ES
UDC.startPage467es_ES
UDC.endPage489es_ES
dc.identifier.doi10.12694/scpe.v16i4.1134


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