You are here

QUARTERMASTER: Query and User-based Abductive Reporting Tool Enabling Responsive Multimodal Analysis of Simulated Technological Enterprise Records

Award Information
Agency: Department of Defense
Branch: Missile Defense Agency
Contract: HQ0860-20-C-7068
Agency Tracking Number: B19C-003-0011
Amount: $124,981.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: MDA19-T003
Solicitation Number: 19.C
Solicitation Year: 2019
Award Year: 2020
Award Start Date (Proposal Award Date): 2020-04-06
Award End Date (Contract End Date): 2021-10-05
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
 Brent Fegley
 Senior Research Engineer
 (321) 710-3209
Business Contact
 Thomas McKenna
Phone: (781) 496-2443
Research Institution
 Brandeis University
 Dr. James Pustejovsky Dr. James Pustejovsky
415 South Street MS 116
Waltham, MA 02453
United States

 (781) 736-2709
 Nonprofit College or University

At the Missile Defense Agency (MDA), the system-wide approach to test engineering generates massive amounts of multi-dimensional data, which analysts explore to evaluate Ballistic Missile Defense System (BMDS) configurations, engagement conditions, and target phenomena. Analyses are limited by time to construct each data query, technical knowledge, and size/complexity of the datasets and domain. To overcome these constraints, Aptima and Brandeis University propose to develop QUARTERMASTER (Query and User-based Abductive Reporting Tool Enabling Responsive Multimodal Analysis of Simulated Technological Enterprise Records). As an interactive cognitive assistant, it will leverage natural language processing, a domain ontology, causal inferences about users’ questions, and causal inference analyses of MDA simulation data to answer written and spoken queries by analysts. QUARTERMASTER will provide direct responses to queries and potential causes explicating the effect identified through abductive reasoning, and suggestions for clarifying simulation experiments should gaps remain in answering a query. QUARTERMASTER will augment MDA capabilities by enabling quick data sifting and understanding, and fast finding of key insights as a precursor to deeper, focused analyses. Ultimately, QUARTERMASTER will empower MDA analysts at all expertise levels to simply and naturally query their datasets, and thus contribute to faster and more complete analyses of BMDS. Approved for Public Release | 20-MDA-10398 (2 Mar 20)

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

US Flag An Official Website of the United States Government