File:MACHINE LEARNING OF EXTREMELY LARGE SETS OF SIGNAL COLLECTIONS USING CLUSTER COMPUTING (IA machinelearningo1094564153).pdf
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MACHINE LEARNING OF EXTREMELY LARGE SETS OF SIGNAL COLLECTIONS USING CLUSTER COMPUTING
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| Author |
Ferris, Christopher L. |
| Title |
MACHINE LEARNING OF EXTREMELY LARGE SETS OF SIGNAL COLLECTIONS USING CLUSTER COMPUTING |
| Publisher |
Monterey, CA; Naval Postgraduate School |
| Description |
Multitudes of signals are transmitted over the airwaves at any given moment, creating a large intelligence opportunity and reconnaissance problem. As technology advances, cluster computing methods must be explored to fill the intelligence gap caused by an increasingly large amount of data and a limited number of human analysts. In this thesis, Apache HBase, Phoenix, and Spark are employed on an AWS EMR cluster to store, query, and implement the K-means machine learning algorithm on a large-scale signals database. The signal databases tested consist of up to 100 million randomly generated signals, with nine feature columns of metadata. The signal data set is first bulk-loaded into HBase and a Phoenix layer is implemented. The data is then queried from Spark into a Dataframe for machine learning implementation. Additionally, the K-means implementations are run on multiple different computer-cluster configurations to test performance as a function of the number of computers in the cluster. This thesis demonstrates the capabilities and benefits of utilizing open-source software and cluster computing to implement large-scale machine learning on signal metadata. Subjects: machine learning; cluster computing; signal collection; signal analysis |
| Language | English |
| Publication date | December 2019 |
| Current location |
IA Collections: navalpostgraduateschoollibrary; fedlink |
| Accession number |
machinelearningo1094564153 |
| Source | |
| Permission (Reusing this file) |
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States. |
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| This file has been identified as being free of known restrictions under copyright law, including all related and neighboring rights. | ||
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| current | 16:55, 22 July 2020 |  | 1,275 × 1,650, 90 pages (3.23 MB) | Fæ (talk | contribs) | FEDLINK - United States Federal Collection machinelearningo1094564153 (User talk:Fæ/IA books#Fork8) (batch 1993-2020 #20989) |
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