| Autor: |
Diego Fustes, D. Garabato, Bernardino Arcay, Carlos Dafonte, Minia Manteiga, M. A. Álvarez |
| Rok vydání: |
2015 |
| Předmět: |
|
| Zdroj: |
IFSA-EUSFLAT |
| ISSN: |
1951-6851 |
| DOI: |
10.2991/ifsa-eusflat-15.2015.126 |
| Popis: |
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 dedicated 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. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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