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Astrophysics and Big Data: Challenges, Methods, and Tools

Published online by Cambridge University Press:  30 May 2017

Mauro Garofalo Open the ORCID record for Mauro Garofalo [Opens in a new window] ,
Alessio Botta  and
Giorgio Ventre
Mauro Garofalo
Affiliation:
DIETI, University of Naples Federico II, Via Claudio, 21 I-80125 Napoli, Italy email: [email protected], [email protected], [email protected]
Alessio Botta
Affiliation:
DIETI, University of Naples Federico II, Via Claudio, 21 I-80125 Napoli, Italy email: [email protected], [email protected], [email protected] NM2 srl, Napoli, Italy
Giorgio Ventre
Affiliation:
DIETI, University of Naples Federico II, Via Claudio, 21 I-80125 Napoli, Italy email: [email protected], [email protected], [email protected]
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Abstract

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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.

Keywords

methods: data analysismethods: statistical
Type
Contributed Papers
Information
Proceedings of the International Astronomical Union , Volume 12 , Symposium S325: Astroinformatics , October 2016 , pp. 345 - 348
Copyright
Copyright © International Astronomical Union 2017 

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