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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Bibliographic Details
Published in:Journal of ambient intelligence and humanized computing Vol. 10; no. 9; pp. 3383 - 3398
Main Authors: Cuzzocrea, Alfredo, Gaber, Mohamed Medhat, Fadda, Edoardo, Grasso, Giorgio Mario
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2019
Springer Nature B.V
Subjects:
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 Access:Get full text
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Summary: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 Gas Emissions (GGEs), a relevant instance of Big Data that empathize the Variety aspect of the well-known 3V Big Data axioms. A relevant case study is also introduced and discussed in detail. We also provide a comprehensive experimental evaluation of the proposed framework, which indeed confirms the benefits of our approach. The deriving Big Data Mining model turns to be useful for decision support processes in both the governmental and industrial contexts. We complete our analytical contributions by means of concluding remarks of our work, and a vision on future research efforts in the field.
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ISSN:1868-5137
1868-5145
DOI:10.1007/s12652-018-0966-1