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MAVERICK: Mixed-Automation Visualizer for Emerging Relationships & Insights in Complex Knowledge

Award Information
Agency: Department of Defense
Branch: Missile Defense Agency
Contract: HQ0147-19-C-7057
Agency Tracking Number: B18C-001-0073
Amount: $124,989.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: MDA18-T001
Solicitation Number: 18.C
Timeline
Solicitation Year: 2018
Award Year: 2019
Award Start Date (Proposal Award Date): 2019-03-26
Award End Date (Contract End Date): 2020-06-25
Small Business Information
12 Gill Street Suite 1400
Woburn, MA 01801
United States
DUNS: 967259946
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Sylvain Bruni
 Senior Human Systems Engineer
 (617) 417-0359
 sbruni@aptima.com
Business Contact
 Thomas McKenna
Phone: (781) 496-2443
Email: brouady@aptima.com
Research Institution
 Central Washington University
 Ms. Ruth Jeffries Ms. Ruth Jeffries
 
400 East University Way
Ellensburg, WA 98926
United States

 (509) 963-2640
 Nonprofit college or university
Abstract

System-level simulations of the Ballistic Missile Defense System (BMDS) generate extremely large quantities of highly multidimensional data, which analysts at the Missile Defense Agency (MDA) must explore and analyze to infer insights and relationships. Existing tools based on automated routine scripts are ill-equipped to handle the volume and complexity of these data, requiring piecemeal analysis and inefficient workarounds. To address this challenge, we propose to develop a Mixed-Automation Visualizer for Emerging Relationships & Insights in Complex Knowledge. This is an integrated visual analytics tool that provides rich, insight-driven, expanded exploration and analysis capabilities to MDA analysts, and relies on recent breakthrough algorithms for dimensionality reduction, interactive visualizations, and mixed-automation support, combined as an integrated and learning cognitive assistant. The visualizer will dually leverage advances in visual data mining and machine learning for adaptable and adaptive automation support. Ultimately, the visualizer will enable efficient and effective interaction with and exploration of large, multidimensional datasets, leading to faster, more complete, and more insightful analyses of BMDS simulation data. Approved for Public Release | 19-MDA-9932 (21 Feb 19)

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

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