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Artificial Intelligence Tools for Data Mining in Large Astronomical Databases

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Toward an International Virtual Observatory

Part of the book series: ESO ASTROPHYSICS SYMPOSIA ((ESO))

Abstract.

The federation of heterogeneous large astronomical databases foreseen in the framework of the AVO and NVO projects will pose unprecedented data mining and visualization problems which may find a rather natural and user friendly answer in artificial intelligence (A.I.) tools based on neural networks, fuzzy-C sets or genetic algorithms. We shortly describe some tools implemented by the AstroNeural collaboration (Napoli-Salerno) aimed to perform complex tasks such as, for instance, unsupervised and supervised clustering and time series analysis. Two very different applications to the analysis of photometric redshifts of galaxies in the Sloan Early Data Release and to the telemetry of the TNG (telescopio nazionale Galileo) are also discussed as template cases.

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Authors and Affiliations

  1. Department of Physical Sciences, University Federico II, via Cinthia, Napoli, Italy

    Giuseppe Longo

  2. INAF - Osservatorio Astronomico di Capodimonte, via Moiariello 16, Napoli, Italy

    Giuseppe Longo & Ciro Donalek

  3. Department of Mathematics and Informatics - DMI, University of Salerno, Baronissi, Italy

    Giancarlo Raiconi, Antonino Staiano, Roberto Tagliaferri & Alfredo Volpicelli

  4. INFM - Section of Salerno, via S. Allende, Baronissi, Italy

    Giancarlo Raiconi & Roberto Tagliaferri

  5. INAF - Osservatorio Astrofisico di Trieste, via Tiepolo 13, Trieste, Italy

    Fabio Pasian & Riccardo Smareglia

  6. Dipartimento di Matematica Applicata, Facoltà di Architettura, Università Federico II, Napoli, Italy

    Salvatore Sessa

Authors
  1. Giuseppe Longo
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  2. Ciro Donalek
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  3. Giancarlo Raiconi
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  4. Antonino Staiano
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  5. Roberto Tagliaferri
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  6. Fabio Pasian
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  7. Salvatore Sessa
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  8. Riccardo Smareglia
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  9. Alfredo Volpicelli
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Corresponding author

Correspondence to Giuseppe Longo .

Editor information

Peter J. Quinn Krzysztof M. Górski

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Longo, G. et al. Artificial Intelligence Tools for Data Mining in Large Astronomical Databases. In: Quinn, P.J., Górski, K.M. (eds) Toward an International Virtual Observatory. ESO ASTROPHYSICS SYMPOSIA. Springer, Berlin, Heidelberg. https://doi.org/10.1007/10857598_31

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  • DOI: https://doi.org/10.1007/10857598_31

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-21001-6

  • Online ISBN: 978-3-540-39908-7

  • eBook Packages: Springer Book Archive

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