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Sensors 2018, 18(5), 1419; https://doi.org/10.3390/s18051419

Distributed Fast Self-Organized Maps for Massive Spectrophotometric Data Analysis

1
CITIC—Department of Computer Science, University of A Coruña, Campus de Elviña s/n, 15071 A Coruña, Spain
2
CITIC—Astronomy and Astrophysics, University of A Coruña, Campus de Elviña s/n, 15071 A Coruña, Spain
This paper is an extended version of our paper published in Garabato, D., Dafonte, C., Álvarez, M. A., Manteiga, M. Distributed Unsupervised Clustering for Outlier Analysis in the Biggest Milky Way Survey: ESA Gaia Mission. In Proceedings of the 11th International Conference on Ubiquitous Computing and Ambient Intelligence, Philadelphia, PA, USA, 7–10 November 2017.
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Authors to whom correspondence should be addressed.
Received: 14 March 2018 / Revised: 26 April 2018 / Accepted: 1 May 2018 / Published: 3 May 2018
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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. View Full-Text
Keywords: remote sensing; computational astrophysics; distributed computing; fast self-organized maps; Apache Hadoop; Apache Spark remote sensing; computational astrophysics; distributed computing; fast self-organized maps; Apache Hadoop; Apache Spark
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Dafonte, C.; Garabato, D.; Álvarez, M.A.; Manteiga, M. Distributed Fast Self-Organized Maps for Massive Spectrophotometric Data Analysis . Sensors 2018, 18, 1419.

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