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

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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.

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Notes

  1. http://unfccc.int.

  2. http://www.epa.gov.

  3. http://unfccc.int/kyoto_protocol/items/2830.php.

  4. http://www.eea.europa.eu.

  5. http://www.cia.gov.

  6. http://www.epa.gov/ozone/science/sc_fact.html.

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Acknowledgements

Authors are very grateful to Dr. Staci Lattimer, who contributed to early versions of this research.

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Authors and Affiliations

  1. DIA Department, University of Trieste and ICAR-CNR, Trieste, Italy

    Alfredo Cuzzocrea

  2. School of Computing Science and Digital Media, Robert Gordon University, Aberdeen, UK

    Mohamed Medhat Gaber

  3. ICT for City Logistics and Enterprises Lab., Politecnico di Torino, Turin, Italy

    Edoardo Fadda

  4. CSESC Department, University of Messina, Messina, Italy

    Giorgio Mario Grasso

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  1. Alfredo Cuzzocrea
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  2. Mohamed Medhat Gaber
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  3. Edoardo Fadda
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  4. Giorgio Mario Grasso
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Correspondence to Edoardo Fadda.

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Cuzzocrea, A., Gaber, M.M., Fadda, E. et al. An innovative framework for supporting big atmospheric data analytics via clustering-based spatio-temporal analysis. J Ambient Intell Human Comput 10, 3383–3398 (2019). https://doi.org/10.1007/s12652-018-0966-1

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