Big Data Analytics for Smart Cities: The H2020 CLASS Project

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dc.contributor	Barcelona Supercomputing Center
dc.contributor.author	Quiñones, Eduardo
dc.contributor.author	Bertogna, Marko
dc.contributor.author	Hadad, Erez
dc.contributor.author	Ferrer, Ana J.
dc.contributor.author	Chiantore, Luca
dc.contributor.author	Reboa, Alfredo
dc.date	2018-06-04
dc.identifier.citation	Quiñones, 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.citation	978-1-4503-5849-1
dc.identifier.citation	10.1145/3211890.3211914
dc.identifier.uri	http://hdl.handle.net/2117/167233
dc.language.iso	eng
dc.publisher	ACM
dc.relation	https://dl.acm.org/citation.cfm?id=3211914
dc.relation	info:eu-repo/grantAgreement/EC/H2020/780622/EU/Edge and CLoud Computation: A Highly Distributed Software Architecture for Big Data AnalyticS/CLASS
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	Àrees temàtiques de la UPC::Informàtica
dc.subject	High performance computing
dc.subject	Cloud computing
dc.subject	Embedded systems
dc.subject	Real-time schedulability
dc.subject	Theory of computation
dc.subject	MapReduce algorithms
dc.subject	Supercomputadors
dc.title	Big Data Analytics for Smart Cities: The H2020 CLASS Project
dc.type	info:eu-repo/semantics/submittedVersion
dc.type	info:eu-repo/semantics/conferenceObject
dc.description.abstract	Applying 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.abstract	The 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.abstract	Peer Reviewed

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