Distributed Fast Self-Organized Maps for Massive Spectrophotometric Data Analysis
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Distributed Fast Self-Organized Maps for Massive Spectrophotometric Data AnalysisFecha
2018-05-03Cita bibliográfica
Dafonte, C.; Garabato, D.; Álvarez, M.A.; Manteiga, M. Distributed Fast Self-Organized Maps for Massive Spectrophotometric Data Analysis †. Sensors 2018, 18, 1419.
Resumen
[Abstract] Analyzing huge amounts of data becomes essential in the era of Big Data, where databases are populated with hundreds of Gigabytes that must be processed to extract knowledge. Hence, classical algorithms must be adapted towards distributed computing methodologies that leverage the underlying computational power of these platforms. Here, a parallel, scalable, and optimized design for self-organized maps (SOM) is proposed in order to analyze massive data gathered by the spectrophotometric sensor of the European Space Agency (ESA) Gaia spacecraft, although it could be extrapolated to other domains. The performance comparison between the sequential implementation and the distributed ones based on Apache Hadoop and Apache Spark is an important part of the work, as well as the detailed analysis of the proposed optimizations. Finally, a domain-specific visualization tool to explore astronomical SOMs is presented.
Palabras clave
Remote sensing
Computational astrophysics
Distributed computing
Fast self-organized maps
Apache Hadoop
Apache Spark
Computational astrophysics
Distributed computing
Fast self-organized maps
Apache Hadoop
Apache Spark
Versión del editor
Derechos
Atribución 4.0 Internacional
ISSN
1424-8220