A distributed learning algorithm for Self-Organizing Maps intended for outlier analysis in the GAIA – ESA mission
Daniel Garabato, Carlos Dafonte, Minia Manteiga, Diego Fustes, Marco A. Álvarez, Bernardino Arcay
Available Online June 2015.
- https://doi.org/10.2991/ifsa-eusflat-15.2015.126How to use a DOI?
- Gaia mission, self-organizing maps, distributed computing, Hadoop.
- 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
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 - https://doi.org/10.2991/ifsa-eusflat-15.2015.126 ID - Garabato2015/06 ER -