DATA MANAGEMENT FOR ARTIFICIAL INTELLIGENCE MACHINE LEARNING IMPLEMENTATION ACROSS THE DEPARTMENT OF THE NAVY
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Authors
French, Robert
Fukumae, Wallace Y., Jr.
Hun, Kheng S.
Matuga, Obed
O'Shaughnessy, Caitlyn R.
Subjects
artificial intelligence
machine learning
AI/ML
data
machine learning
AI/ML
data
Advisors
Johnson, Bonnie W.
Date of Issue
2021-06
Date
Publisher
Monterey, CA; Naval Postgraduate School
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Abstract
The private sector continuously harvests and curates key data and its sources so as to ensure the support and development of new operational insights, generated by leveraging data-intensive artificial intelligence machine learning (AI/ML) techniques. Industry culture affirms that all data are valued shared resources, an approach the Navy so far has failed to realize. This capstone explores the Navy's challenging task of creating data availability and quality through research, interviews, and personal expertise. Research focuses on process, technology, and governance, employing a detailed needs assessment, stakeholders' analysis, and functional design. The result is a conceptual framework for a centralized artificial intelligence library (CAIL), designed to match industry’s resolute attention to data as a critical commodity. The Navy needs persistent and dynamic digital readiness, so this capstone team, with over 70 years of combined United States naval data expertise, recommends that OVERMATCH consider these findings and generate a system that ensures data availability and quality for the Navy.
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Distribution Statement
Approved for public release. Distribution is unlimited.
Rights
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.