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.

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

Grasso, Giorgio Mario
2019-01-01

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.
2019
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11570/3164462
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