An innovative framework for supporting big atmospheric data analytics via clustering-based spatio-temporal analysis

In this paper, we provide principles, models, and main architecture of an innovative framework for supporting intelligent analytics over big atmospheric data via clustering-based spatio-temporal analysis . In particular we investigates the interesting applicative setting represented by Greenhouse Ga...

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Vydáno v:	Journal of ambient intelligence and humanized computing Ročník 10; číslo 9; s. 3383 - 3398
Hlavní autoři:	Cuzzocrea, Alfredo, Gaber, Mohamed Medhat, Fadda, Edoardo, Grasso, Giorgio Mario
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2019 Springer Nature B.V
Témata:	Artificial Intelligence Atmospheric models Axioms Big Data Clustering Computational Intelligence Data analysis Data mining Decision making Emissions Engineering Greenhouse gases Mathematical analysis Original Research Robotics and Automation Sensors Smart cities Spatial analysis User Interfaces and Human Computer Interaction United Kingdom > UK Big data mining Big environmental and atmospheric data Clustering-based spatio-temporal analysis of big data
ISSN:	1868-5137, 1868-5145
On-line přístup:	Získat plný text
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