File:SIMPLIFYING DATA ANALYSIS FOR SUBJECT MATTER EXPERTS (IA simplifyingdataa1094561292).pdf
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SIMPLIFYING DATA ANALYSIS FOR SUBJECT MATTER EXPERTS
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| Author |
Vanzant, Timberon C. |
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| Title |
SIMPLIFYING DATA ANALYSIS FOR SUBJECT MATTER EXPERTS |
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| Publisher |
Monterey, CA; Naval Postgraduate School |
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| Description |
In today’s data-intensive world, the power to analyze huge amounts of data is critical to the success of any organization, including the military. Many data analysis tools have been developed in the past decade along with the high-performance machine learning algorithms. At present, many of these tools unfortunately are out of reach of the target audience—subject matter experts—because one must master some of the advanced computer science concepts to use these tools effectively. This thesis proposes to build a prototype data analysis platform that will hide the underlying complexity of the tools from the subject matter experts. Using the platform, the end users can analyze data through a simple, menu-driven interface. The prototype will be built using the programming language Python and the open-source, distributed data processing engine Apache Spark 2.0. Different components of Spark 2.0 will be studied and evaluated to determine the best approach for building the prototype. The effectiveness of the prototype will be examined using the ADSB (Automatic Dependent Surveillance - Broadcast) unfiltered flight data. The thesis concludes with the review of the prototype developed for ADSB and the recommendation on possible ways of extending the prototype. Subjects: data analysis; machine learning; Spark |
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| Language | English | |
| Publication date | December 2018 | |
| Current location |
IA Collections: navalpostgraduateschoollibrary; fedlink |
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| Accession number |
simplifyingdataa1094561292 |
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| 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 work is in the public domain in the United States because it is a work prepared by an officer or employee of the United States Government as part of that person’s official duties under the terms of Title 17, Chapter 1, Section 105 of the US Code.
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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 | 15:54, 24 July 2020 |  | 1,275 × 1,650, 72 pages (1.42 MB) | Fæ (talk | contribs) | FEDLINK - United States Federal Collection simplifyingdataa1094561292 (User talk:Fæ/IA books#Fork8) (batch 1993-2020 #27536) |
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