Clustering-Based Spatio-Temporal Analysis of Big Atmospheric Data

Abstract

This paper proposes a comprehensive approach for supporting clustering-based spatio-temporal analysis of big atmospheric data via specializing on 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. In particular, in our research we consider GGEs from three EU countries, namely UK, France and Italy. The deriving Big Data Mining model turns to be useful for decision support processes in both the governmental and industrial contexts.