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Virtual Observatories, Data Mining, and Astroinformatics

  • Kirk Borne

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.

Keywords

Astroinformatics bayesian classification classification clustering data management data mining data preparation data profiling data science data transformation databases decision tree distance metrics e-Science exploratory data analysis fourth paradigm informatics K-means machine learning neural network outlier detection semantic science semisupervised learning similarity metrics sky surveys supervised learning survey science unsupervised learning virtual observatory visualization VOEvent 

Abbreviations

List of 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

Notes

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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© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Kirk Borne
    • 1
  1. 1.George Mason UniversityFairfaxUSA

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