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Extracting Knowledge from Massive Astronomical Data Sets

Brescia, Massimo and Cavuoti, Stefano and Djorgovski, George S. and Donalek, Ciro and Longo, Giuseppe and Paolillo, Maurizio (2012) Extracting Knowledge from Massive Astronomical Data Sets. In: Astrostatistics and Data Mining. Springer Series in Astrostatistics. No.2. Springer , New York, NY, pp. 31-45. ISBN 978-1-4614-3322-4.

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The exponential growth of astronomical data collected by both ground-based and spaceborne instruments has fostered the growth of astroinformatics: a new discipline lying at the intersection between astronomy, applied computer science, and information and computation technologies. At the very heart of astroinformatics is a complex set of methodologies usually called data mining (DM) or knowledge discovery in databases (KDD). In the astronomical domain, DM/KDD are still in a very early usage stage, even though new methods and tools are being continuously deployed to cope with the massive data sets (MDSs) that can only grow in the future. In this paper, we briefly outline some general problems encountered when applying DM/KDD methods to astrophysical problems and describe the DAME (Data Mining and Exploration) Web application. While specifically tailored to work on MDSs, DAME can be effectively applied also to smaller data sets. As an illustration, we describe two applications of DAME to two different problems: the identification of candidate GCs in external galaxies and the classification of active Galactic nuclei (AGN). We believe that tools and services of this nature will become increasingly necessary for data-intensive astronomy (and indeed all sciences) in the twenty-first century.

Item Type:Book Section
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URLURL TypeDescription
Brescia, Massimo0000-0001-9506-5680
Cavuoti, Stefano0000-0002-3787-4196
Djorgovski, George S.0000-0002-0603-3087
Longo, Giuseppe0000-0002-9182-8414
Paolillo, Maurizio0000-0003-4210-7693
Additional Information:© 2012 Springer Science+Business Media New York. First Online: 05 June 2012. The DAME Web application was funded in part by the Italian Ministry of Foreign Affairs through bilateral projects between Italy and the USA and by the Italian Ministry of Education, Universities, and Research through the PON 1575 S.Co.P.E. SGD and CD acknowledge partial support through NASA Grant 08-AISR08-0085, NSF Grants AST-0834235 and AST-0909182, and the Fishbein Family Foundation. We thank numerous collaborators for many interesting discussions on these and related issues over the years.
Funding AgencyGrant Number
Ministero degli affari esteri e della cooperazione internazionale (MAECI)UNSPECIFIED
Ministero dell'Istruzione, dell'Università e della Ricerca (MIUR)PON 1575 S.Co.P.E.
Fishbein Family FoundationUNSPECIFIED
Series Name:Springer Series in Astrostatistics
Issue or Number:2
Record Number:CaltechAUTHORS:20190723-141622366
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:97361
Deposited By: Tony Diaz
Deposited On:23 Jul 2019 22:11
Last Modified:16 Nov 2021 17:31

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