Situational Awareness as a Man-Machine Map Reduce Job

Award Information
Agency:
Department of Defense
Amount:
$79,774.00
Program:
STTR
Contract:
N00014-13-P-1179
Solitcitation Year:
2013
Solicitation Number:
2013.A
Branch:
Navy
Award Year:
2013
Phase:
Phase I
Agency Tracking Number:
N13A-024-0113
Solicitation Topic Code:
N13A-T024
Small Business Information
Soar Technology, Inc.
3600 Green Court, Suite 600, Ann Arbor, MI, -
Hubzone Owned:
N
Woman Owned:
N
Socially and Economically Disadvantaged:
N
Duns:
009485124
Principal Investigator
 Sven Brueckner
 Lead Researcher
 (734) 887-7642
 sven.brueckner@soartech.com
Business Contact
 Andrew Dallas
Title: Vice President
Phone: (734) 887-7603
Email: contracts@soartech.com
Research Institution
 CUBRC
 Adam Stotz
 4455 Genessee Street
Buffalo, NY, 14225-
 (716) 204-5123
 Nonprofit college or university
Abstract
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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