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Data Retrieval Assistant for Consistent simulation data exploration using Natural Language interfaces (DRACO)

Award Information
Agency: Department of Defense
Branch: Missile Defense Agency
Contract: HQ0860-20-C-7070
Agency Tracking Number: B19C-003-0097
Amount: $125,000.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: MDA19-T003
Solicitation Number: 19.C
Timeline
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
1408 University Drive East
College Station, TX 77840
United States
DUNS: 555403328
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Perakath Benjamin
 Senior Research Scientist
 (979) 260-5274
 pbenjamin@kbsi.com
Business Contact
 Jason Ogle
Phone: (979) 260-5274
Email: jogle@kbsi.com
Research Institution
 Texas A&M University
 James Caverly James Caverly
 
3112 TAMU
College Station, TX 77843
United States

 (979) 845-5534
 Nonprofit College or University
Abstract

KBSI proposes to establish an innovative solution approach to support the analysis and enhanced understanding of federated simulation output data in a manner that consistently increases data analysts’ efficiency and effectiveness. Specifically, KBSI will research, design, and demonstrate the Data Retrieval Assistant for Consistent simulation data exploration using Natural Language interfaces (DRACO)’ system. DRACO will (i) accept natural language inputs, (ii) use semantic methods to auto-generate database queries, (iii) integrate the query results, and (iv) provide advanced and dynamic human machine interface (HMI) mechanisms to enable intuitive end user understanding and insight. The Phase I project will research, design, and demonstrate the technical feasibility of the DRACO solution approach. Phase II will refine, enhance, validate, and mature DRACO leading to a focused MDA application, leading to rapid technology transition and commercialization. Important innovations include (i) semantic disambiguation methods for processing Natural Language questions and commands, (ii) ontology based approach for automated query generation, (iii) rule based methods for query output fusion, (iv) dynamic and interactive human machine interfaces for enhanced sense making and human understanding, and (v) automated learning methods to enable adaptation of the search and exploration to different users and to dynamically changing BMDS data and ontologies. Approved for Public Release | 20-MDA-10398 (2 Mar 20)

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

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