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Revolutionary Data Presentation and Manipulation (REVOLT)

Award Information
Agency: Department of Defense
Branch: Missile Defense Agency
Contract: HQ0147-19-C-7055
Agency Tracking Number: B18C-001-0010
Amount: $99,985.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: MDA18-T001
Solicitation Number: 18.C
Solicitation Year: 2018
Award Year: 2019
Award Start Date (Proposal Award Date): 2019-03-26
Award End Date (Contract End Date): 2019-09-25
Small Business Information
625 Mount Auburn Street
Cambridge, MA 02138
United States
DUNS: 115243701
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Alexander Gee
 Senior Software Engineer
 (617) 491-3474
Business Contact
 Yvonne Fuller
Phone: (617) 491-3474
Research Institution
 Georgia Institute of Technology
 Mr. Timothy Gehret, Esq. Mr. Timothy Gehret, Esq.
505 Tenth Street N.W.
Atlanta, GA 30332
United States

 (404) 594-0950
 Nonprofit College or University

Successful missile defense is critical to the security of the United States, its deployed forces, and its allies. Across numerous simulations, tests, and operational scenarios, the Government amasses large quantities of complex high-dimensional data describing missile defense outcomes. While Missile Defense Agency (MDA) analysts need to use this data to better understand and improve missile defense capabilities and plans, the size and complexity of the data make it difficult to extract and comprehend these often complex and subtle phenomena. The MDA needs a revolutionary approach to visualize and understand operationally relevant outcomes, and their implications for current and future missile defense applications. To address this need, we propose to design and demonstrate Revolutionary Evaluations and Visualizations for Observing Contextual Patterns in Large-Scale Data (REVOLT), a guided system for extracting, visualizing and understanding mission-centric information patterns in large-scale, multi-source, high-dimensional missile defense data. REVOLT will: (1) define contextually oriented interactive data facts that guide analysts to critical information patterns within the data; (2) use ecological interface design to develop revolutionary visualization techniques for the understanding of large-scale, high-dimensional data; and (3) provide a contextual graphical grammar that maps data to effective visualization techniques to reveal information patterns. Approved for Public Release | 19-MDA-9932 (21 Feb 19)

* Information listed above is at the time of submission. *

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