Big Data Analytics for Smart Cities: The H2020 CLASS Project

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ProjectCLASS - Edge and CLoud Computation: A Highly Distributed Software Architecture for Big Data AnalyticS (EC-H2020-780622)

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.

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.

URIhttp://hdl.handle.net/2117/167233

DOI10.1145/3211890.3211914

ISBN978-1-4503-5849-1

Publisher versionhttps://dl.acm.org/citation.cfm?id=3211914

Collections

Computer Sciences - Ponències/Comunicacions de congressos [596]

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