Publicación: Estudio y análisis de las técnicas del pipeline de OCA aplicadas a datos simulados de la misión GAIA
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2012-09-27
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info:eu-repo/semantics/openAccess
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Universidad Nacional de Educación a Distancia (España). Escuela Técnica Superior de Ingeniería Informática. Departamento de Inteligencia Artificial.
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Este Trabajo de Fin de Master se enmarca dentro de la mision GAIA de la Agencia Europea del Espacio, cuyo lanzamiento esta previsto para nales de 2013. Debido al elevado volumen de datos que se generara, se ha decidido crear un consorcio para el procesamiento y analisis de datos, llamado Gaia Data Processing and Analysis Consortium (DPAC). Dicho consorcio se encuentra dividido en 10 unidades de coordinacion de las cuales, la octava (CU8), esta enfocada en la estimacion de parametros astrofsicos a partir de las observaciones. El presente trabajo pertenece a los estudios preliminares llevados a cabo dentro del bloque de trabajo Object Clustering Algorithm (OCA) de CU8. El objetivo principal de OCA es desarrollar las herramientas adecuadas para el analisis de los datos recibidos desde la perspectiva del agrupamiento. Los principales retos de OCA son estudiar la tecnicas mas adecuadas de agrupamiento para la naturaleza de los datos de GAIA y ser capaz de escalar dichas tecnicas para poder tratar el gran volumen de datos que se recibira a lo largo de la mision. A lo largo de este trabajo se presentaran las metodologas propuestas en la literatura para poder escalar algoritmos de agrupamiento as como la descripcion de las tecnicas elegidas debido a sus propiedades.
This Final Master Work is part of the GAIA mission of the European Space Agency, which is scheduled for late 2013. Due to the high volume of data being generated, it was decided to create an international consortium in charge of Gaia data processing, called Gaia Data Processing and Analysis Consortium (DPAC). It is formed around a set of 10 Coordination Units, the eighth (CU8), is focused on the estimation of astrophysical parameters. This work is part of the preliminary studies carried out within the working group Object Clustering Algorithm (OCA) of CU8. The main objective of OCA is to develop appropriate tools for analyzing data received from the clustering perspective. OCA's main challenges are to study clustering techniques suitable to the nature of the GAIA data and be able to scale these techniques to deal with the large volume of data to be received over the mission. The methodologies proposed in the literature to be able to scale clustering algorithms, as well as descriptions of the techniques chosen due to their properties will be presented throughout this work.
This Final Master Work is part of the GAIA mission of the European Space Agency, which is scheduled for late 2013. Due to the high volume of data being generated, it was decided to create an international consortium in charge of Gaia data processing, called Gaia Data Processing and Analysis Consortium (DPAC). It is formed around a set of 10 Coordination Units, the eighth (CU8), is focused on the estimation of astrophysical parameters. This work is part of the preliminary studies carried out within the working group Object Clustering Algorithm (OCA) of CU8. The main objective of OCA is to develop appropriate tools for analyzing data received from the clustering perspective. OCA's main challenges are to study clustering techniques suitable to the nature of the GAIA data and be able to scale these techniques to deal with the large volume of data to be received over the mission. The methodologies proposed in the literature to be able to scale clustering algorithms, as well as descriptions of the techniques chosen due to their properties will be presented throughout this work.
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Facultades y escuelas::E.T.S. de Ingeniería Informática
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Inteligencia Artificial