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27 April 2024 |
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Astrophysics and Big Data: Challenges, Methods, and Tools | | Mauro Garofalo
; Alessio Botta
; Giorgio Ventre
; | | Date: |
15 Mar 2017 | | 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. | | Source: | arXiv, 1703.5084 | | Services: | Forum | Review | PDF | Favorites | |
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