Data-Rich Astronomy: Mining Synoptic Sky Surveys - Cavuoti, Stefano
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In the last decade a new generation of telescopes and sensors has allowed the production of a very large amount of data and astronomy has become, a data-rich science; this transition is often labeled as: "data revolution" and "data tsunami". The first locution puts emphasis on the expectations of the astronomers while the second stresses, instead, the dramatic problem arising from this large amount of data: which is no longer computable with traditional approaches to data storage, data reduction and data analysis. In a new, age new instruments are necessary, as it happened in the Bronze age…mehr

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Produktbeschreibung
In the last decade a new generation of telescopes and sensors has allowed the production of a very large amount of data and astronomy has become, a data-rich science; this transition is often labeled as: "data revolution" and "data tsunami". The first locution puts emphasis on the expectations of the astronomers while the second stresses, instead, the dramatic problem arising from this large amount of data: which is no longer computable with traditional approaches to data storage, data reduction and data analysis. In a new, age new instruments are necessary, as it happened in the Bronze age when mankind left the old instruments made out of stone to adopt the new, better ones made with bronze. Everything changed, even the social structure. In a similar way, this new age of Astronomy calls for a new generation of tools and, for a new methodological approach to many problems, and for the acquisition of new skills. The attempts to find a solution to this problems falls under the umbrella of a new discipline which originated by the intersection of astronomy, statistics and computer science: Astroinformatics, (Borne, 2009; Djorgovski et al., 2006).
Autorenporträt
Stefano Cavuoti (Salerno, 07 January 1982) is a post doctoral fellow at the INAF ¿ OACN. First Italian to discuss a PhD Thesis in AstroInformatics. He is an expert in astronomical applications of DM data mining methods.