Astrophysics and Big Data: Challenges, Methods, and Tools. Issue Volume 12:Issue S325(2016) (30th May 2017)

• Record Type: Journal Article
• Authors: Garofalo, Mauro; Botta, Alessio; Ventre, Giorgio
• Editors: Brescia, M.; Djorgovski, S.G.; Feigelson, E.; Longo, G.; Cavuoti, S.
• Is Part Of: Proceedings of the International Astronomical Union. Volume 12:Issue S325(2016)
• Page Start: 345
• Page End: 348
• Publication Date: 2017-05-30
• Subjects: methods: data analysis, -- methods: statistical; Astronomy -- Congresses; Astronomy -- Periodicals; 520
• DOI: 10.1017/S1743921316012813
• Languages: English
• ISSNs: 1743-9213

Abstract

Abstract: Nowadays there is no field research which is not flooded with data. Among the sciences, astrophysics has always been driven by the analysis of massive amounts of data. The development of new and more sophisticated observation facilities, both ground-based and spaceborne, has led data more and more complex (Variety), an exponential growth of both data Volume (i.e., in the order of petabytes), and Velocity in terms of production and transmission. Therefore, new and advanced processing solutions will be needed to process this huge amount of data. We investigate some of these solutions, based on machine learning models as well as tools and architectures for Big Data analysis that can be exploited in the astrophysical context.