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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| Published in: | Journal of ambient intelligence and humanized computing Vol. 10; no. 9; pp. 3383 - 3398 |
|---|---|
| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Berlin/Heidelberg
Springer Berlin Heidelberg
01.09.2019
Springer Nature B.V |
| Subjects: | |
| ISSN: | 1868-5137, 1868-5145 |
| Online Access: | Get full text |
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| Abstract | 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. |
|---|---|
| AbstractList | 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. 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. |
| Author | Gaber, Mohamed Medhat Fadda, Edoardo Cuzzocrea, Alfredo Grasso, Giorgio Mario |
| Author_xml | – sequence: 1 givenname: Alfredo surname: Cuzzocrea fullname: Cuzzocrea, Alfredo organization: DIA Department, University of Trieste and ICAR-CNR – sequence: 2 givenname: Mohamed Medhat surname: Gaber fullname: Gaber, Mohamed Medhat organization: School of Computing Science and Digital Media, Robert Gordon University – sequence: 3 givenname: Edoardo orcidid: 0000-0002-5599-6349 surname: Fadda fullname: Fadda, Edoardo email: edoardo.fadda@polito.it organization: ICT for City Logistics and Enterprises Lab., Politecnico di Torino – sequence: 4 givenname: Giorgio Mario surname: Grasso fullname: Grasso, Giorgio Mario organization: CSESC Department, University of Messina |
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| Keywords | Big data mining Big environmental and atmospheric data Clustering-based spatio-temporal analysis of big data |
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big atmospheric data... In this paper, we provide principles, models, and main architecture of an innovative framework for supporting intelligent analytics over big atmospheric data... |
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| SubjectTerms | 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 |
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| Title | An innovative framework for supporting big atmospheric data analytics via clustering-based spatio-temporal analysis |
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