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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Veröffentlicht in:	Journal of ambient intelligence and humanized computing Jg. 10; H. 9; S. 3383 - 3398
Hauptverfasser:	Cuzzocrea, Alfredo, Gaber, Mohamed Medhat, Fadda, Edoardo, Grasso, Giorgio Mario
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2019 Springer Nature B.V
Schlagworte:	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
Online-Zugang:	Volltext
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