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Planets, Stars and Stellar Systems pp 403–443Cite as

Virtual Observatories, Data Mining, and Astroinformatics

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Abstract

The historical, current, and future trends in knowledge discovery from data in astronomy are presented here. The story begins with a brief history of data gathering and data organization. A description of the development of new information science technologies for astronomical discovery is then presented. Among these are e-Science and the virtual observatory, with its data discovery, access, display, and integration protocols; astroinformatics and data mining for exploratory data analysis, information extraction, and knowledge discovery from distributed data collections; new sky surveys’ databases, including rich multivariate observational parameter sets for large numbers of objects; and the emerging discipline of data-oriented astronomical research, called astroinformatics. Astroinformatics is described as the fourth paradigm of astronomical research, following the three traditional research methodologies: observation, theory, and computation/modeling. Astroinformatics research areas include machine learning, data mining, visualization, statistics, semantic science, and scientific data management. Each of these areas is now an active research discipline, with significant science-enabling applications in astronomy. Research challenges and sample research scenarios are presented in these areas, in addition to sample algorithms for data-oriented research. These information science technologies enable scientific knowledge discovery from the increasingly large and complex data collections in astronomy. The education and training of the modern astronomy student must consequently include skill development in these areas, whose practitioners have traditionally been limited to applied mathematicians, computer scientists, and statisticians. Modern astronomical researchers must cross these traditional discipline boundaries, thereby borrowing the best of breed methodologies from multiple disciplines. In the era of large sky surveys and numerous large telescopes, the potential for astronomical discovery is equally large, and so the data-oriented research methods, algorithms, and techniques that are presented here will enable the greatest discovery potential from the ever-growing data and information resources in astronomy.

Somewhere, something incredible is waiting to be known.

Carl Sagan

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Abbreviations

2MASS :

2-Micron All-Sky Survey

AAO :

Anglo-Australian Observatory

ADAC :

Astronomical Data Archives Center (Japan)

ADASS :

Astronomical Data Analysis Software and Systems

ADS :

Astronomical Data Center

ApJS :

Astrophysical Journal Supplement

ANN :

Artificial neural network

BD :

Bonner Durchmusterung

CADC :

Canadian Astronomy Data Center

CDS :

Center de Donnees astronomique de Strasbourg (France)

GCVS :

General Catalog of Variable Stars

DDM :

Distributed data mining

DMD :

Distributed mining of data

DOE :

Department of Energy

DSS :

Digital Sky Survey

EDA :

Exploratory data analysis

HD :

Henry Draper

HEASARC :

High Energy Astrophysics Science Archive Research Center

IPAC :

Infrared Processing and Analysis Center

IRSA :

Infrared Science Archive

IVAO :

International Virtual Observatory Alliance

KDD :

Knowledge Discovery in Databases

KNN :

K-nearest neighbors

LEDAS :

Leicester Database and Archive Service (UK)

LSST :

Large Synoptic Survey Telescope

MAST :

Multimission Archive at Space Telescope

MDD :

Mining of distributed data

ML :

Machine learning

NASA :

National Aeronautics and Space Administration

NED :

NASA/IPAC Extragalactic Database

NGC :

New General Catalog

NSF :

National Science Foundation

NVO :

National Virtual Observatory

Pan-STARRS :

Panoramic Survey Telescope and Rapid Response System

PB :

Petabyte

PDMP :

Project Data Management Plan

PI :

Principal investigator

RA/Dec :

Right ascension and declination

RDF :

Resource Description Framework

SAO :

Smithsonian Astrophysical Observatory

SIMBAD :

Set of Identifications, Measurements, and Bibliography for Astronomical Data

SDSS :

Sloan Digital Sky Survey

SVM :

Support vector machine

TB :

Terabyte

VAO :

Virtual Astronomy Observatory

TMSS :

Two-Micron Sky Survey

VO :

Virtual observatory

WWW :

World Wide Web

XML :

eXtensible Markup Language

ADS :

http://www.adsabs.harvard.edu/

CDS :

http://cdsweb.u-strasbg.fr/

HEASARC :

http://heasarc.gsfc.nasa.gov/

IRSA :

http://irsa.ipac.caltech.edu/

IVOA :

http://ivoa.net/

MAST :

http://archive.stsci.edu/

NED :

http://ned.ipac.caltech.edu/

SDSS :

http://www.sdss.org/

SIMBAD :

http://simbad.u-strasbg.fr/simbad/

VAO :

http://www.usvao.org/

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Acknowledgments

This research has been supported in part by NASA AISR grant number NNX07AV70G. The author thanks numerous colleagues for their significant and invaluable contributions to the ideas expressed in this chapter: Jogesh Babu, Douglas Burke, Andrew Connolly, Timothy Eastman, Eric Feigelson, Matthew Graham, Alexander Gray, Norman Gray, Suzanne Jacoby, Thomas Loredo, Ashish Mahabal, Robert Mann, Bruce McCollum, Misha Pesenson, M. Jordan Raddick, Keivan Stassun, Alex Szalay, Tony Tyson, and John Wallin. The author is grateful to Dr. Hillol Kargupta and his research associates for many years of productive collaborations in the field of distributed data mining in virtual observatories.

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  1. George Mason University, 4400 University Drive, 22030, Fairfax, VA, USA

    Dr. Kirk Borne

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  1. Department of Physics & Space Sciences, Florida Institute of Technology, University Boulevard, Melbourne, FL, USA

    Terry D. Oswalt

  2. Labrador Lane, Cockeysville, MD, USA

    Howard E. Bond

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Borne, K. (2013). Virtual Observatories, Data Mining, and Astroinformatics. In: Oswalt, T.D., Bond, H.E. (eds) Planets, Stars and Stellar Systems. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-5618-2_9

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