Massively Scalable Themes, Entities and Relationships (MASTER)

Award Information
Agency:
Department of Defense
Branch:
Defense Advanced Research Projects Agency
Amount:
$99,000.00
Award Year:
2009
Program:
SBIR
Phase:
Phase I
Contract:
W31P4Q-09-C-0219
Agency Tracking Number:
08SB2-0495
Solicitation Year:
2008
Solicitation Topic Code:
SB082-026
Solicitation Number:
2008.2
Small Business Information
DECISIVE ANALYTICS Corporation
1235 South Clark Street, Suite 400, Arlington, VA, 22202
Hubzone Owned:
N
Socially and Economically Disadvantaged:
N
Woman Owned:
N
Duns:
036593457
Principal Investigator
 Ken Smith
 Senior Scientist
 (703) 682-1738
 ken.smith@dac.us
Business Contact
 Kelly McClelland
Title: Director, Corporate Business Office
Phone: (703) 414-5024
Email: kelly.mcclelland@dac.us
Research Institution
N/A
Abstract
Discovering the important evidence in the form of activities, actors and relationships in a sea of open source data requires the ability to extract and correlate seemingly unrelated pieces of data, distinguish that data from the noise of harmless civilian activity and find the hidden attributes and relationships that characterize the true threat. To meet these requirements, the DAC BOBCAT Team proposes a new suite of algorithms that enable current NLP applications to be immediately available across all levels of military intelligence. We call this approach the Massively Scalable Themes, Entities, and Relationships (MASTER). In the MASTER approach, we overcome the scalability limitations of current NLP approaches while also enabling the tactical warfighter to focus queries based on discovered context and relations. The development of the MASTER approach for the tactical warfighter will result in a suite of algorithms that will support all levels of the fight. The current suite of BOBCAT algorithms, which already advance the state of the art in statistical theme discovery, executes on enterprise platforms at the strategic and operational levels. The MASTER algorithms will specifically focus on the tactical levels with limited computing footprints and large amounts of open-source data.

* information listed above is at the time of submission.

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