A distributed learning algorithm for Self-Organizing Maps intended for outlier analysis in the GAIA – ESA mission

DOI

10.2991/ifsa-eusflat-15.2015.126How to use a DOI?

Keywords

Gaia mission, self-organizing maps, distributed computing, Hadoop.

Abstract

Since its launch in December 2013, the Gaia space mission has collected and continues to collect tremendous amounts of information concerning the objects that populate our Galaxy and beyond. The international Gaia Data and Analysis Consortium (DPAC) is in charge of developing computer algorithms that extract and process astrophysical information from these objects. It organizes its work by means of work packages; one of these packages, Outlier Analysis, is ded0icated to the exploration of vast amounts of outlier objects detected during the main classification of the observations. We present a method that is based on Self-Organizing Maps (SOM) and parallelized by means of the Hadoop framework so as to improve its performance. We also compare the execution times of both the sequential and the distributed versions of the algorithm.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Daniel Garabato
AU  - Carlos Dafonte
AU  - Minia Manteiga
AU  - Diego Fustes
AU  - Marco A. Álvarez
AU  - Bernardino Arcay
PY  - 2015/06
DA  - 2015/06
TI  - A distributed learning algorithm for Self-Organizing Maps intended for outlier analysis in the GAIA – ESA mission
BT  - Proceedings of the 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology
PB  - Atlantis Press
SP  - 895
EP  - 901
SN  - 1951-6851
UR  - https://doi.org/10.2991/ifsa-eusflat-15.2015.126
DO  - 10.2991/ifsa-eusflat-15.2015.126
ID  - Garabato2015/06
ER  -