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Situational Awareness as a Man-Machine Map Reduce Job

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N00014-13-P-1179
Agency Tracking Number: N13A-024-0113
Amount: $79,774.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: N13A-T024
Solicitation Number: 2013.A
Solicitation Year: 2013
Award Year: 2013
Award Start Date (Proposal Award Date): 2013-07-01
Award End Date (Contract End Date): 2014-04-30
Small Business Information
3600 Green Court Suite 600
Ann Arbor, MI -
United States
DUNS: 009485124
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Sven Brueckner
 Lead Researcher
 (734) 887-7642
Business Contact
 Andrew Dallas
Title: Vice President
Phone: (734) 887-7603
Research Institution
 Adam Stotz
4455 Genessee Street
Buffalo, NY 14225-
United States

 (716) 204-5123
 Nonprofit College or University

Improving situational awareness and accuracy of decisions in complex missions relying on streaming open-source data requires scalable information extraction and fusion in collaboration between Man and Machine reasoning. SoarTech, with its proven track-record of basic and applied research and transition into actual deployment, will bring forward advanced imagery and text processing technology integrated in a Hadoop-based distribution framework from its academic partners CUBRC and Dr. Corso from the University of Buffalo. The resulting stream of entity and behavior recognition events from large-scale unstructured and uncalibrated raw data from many sources is fused with a continuously adapting agent simulation and extended into probabilistic predictions of alternative futures. Human intelligence and knowledge is effectively integrated throughout the recognition-fusion-prediction process through a task-aware collaboration environment. As equal participants in the ongoing information-fusion process, decision-makers gain a deeper understanding of the current scenario and the impact of decision alternatives, and thus arrive at better decisions. Our proposed objectives for Phase-I include the demonstration of the feasibility of recognition of HADR-mission-relevant entities and behaviors and the feasibility of such recognition and the subsequent fusion/prediction at scale. We also propose to develop initial use-cases, identify target transition partners, and draft key component and system architecture designs.

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

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