Big Data Analytics for Smart Cities: The H2020 CLASS Project

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dc.contributor.authorQuiñones, Eduardo
dc.contributor.authorBertogna, Marko
dc.contributor.authorHadad, Erez
dc.contributor.authorFerrer, Ana J.
dc.contributor.authorChiantore, Luca
dc.contributor.authorReboa, Alfredo
dc.contributor.otherBarcelona Supercomputing Center
dc.date.accessioned2019-07-31T13:13:02Z
dc.date.available2019-07-31T13:13:02Z
dc.date.issued2018-06-04
dc.description.abstractApplying big-data technologies to field applications has resulted in several new needs. First, processing data across a compute continuum spanning from cloud to edge to devices, with varying capacity, architecture etc. Second, some computations need to be made predictable (real-time response), thus supporting both data-in-motion processing and larger-scale data-at-rest processing. Last, employing an event-driven programming model that supports mixing different APIs and models, such as Map/Reduce, CEP, sequential code, etc.
dc.description.peerreviewedPeer Reviewed
dc.description.sponsorshipThe research leading to these results has received funding from the European Union’s Horizon 2020 Programme under the CLASS Project (www.class-project.eu), grant agreement No. 780622.
dc.description.versionPostprint (author's final draft)
dc.format.extent1 p.
dc.identifier.citationQuiñones, E. [et al.]. Big Data Analytics for Smart Cities: The H2020 CLASS Project. A: "SYSTOR '18 Proceedings of the 11th ACM International Systems and Storage Conference". ACM, 2018, p. 130.
dc.identifier.doi10.1145/3211890.3211914
dc.identifier.isbn978-1-4503-5849-1
dc.identifier.urihttps://hdl.handle.net/2117/167233
dc.language.isoeng
dc.publisherACM
dc.relation.projectidinfo:eu-repo/grantAgreement/EC/H2020/780622/EU/Edge and CLoud Computation: A Highly Distributed Software Architecture for Big Data AnalyticS/CLASS
dc.relation.publisherversionhttps://dl.acm.org/citation.cfm?id=3211914
dc.rights.accessOpen Access
dc.subjectÀrees temàtiques de la UPC::Informàtica
dc.subject.lcshHigh performance computing
dc.subject.lemacSupercomputadors
dc.subject.otherCloud computing
dc.subject.otherEmbedded systems
dc.subject.otherReal-time schedulability
dc.subject.otherTheory of computation
dc.subject.otherMapReduce algorithms
dc.titleBig Data Analytics for Smart Cities: The H2020 CLASS Project
dc.typeConference lecture
dspace.entity.typePublication
local.citation.endingPage130
local.citation.publicationNameSYSTOR '18 Proceedings of the 11th ACM International Systems and Storage Conference
local.citation.startingPage130

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