CEDAR (Complex Event Discovery, Analysis, and Ranking)

Award Information
Agency:
Department of Defense
Branch
Navy
Amount:
$69,994.00
Award Year:
2010
Program:
STTR
Phase:
Phase I
Contract:
N00014-10-M-0288
Award Id:
95175
Agency Tracking Number:
N10A-040-0061
Solicitation Year:
n/a
Solicitation Topic Code:
NAVY 10T040
Solicitation Number:
n/a
Small Business Information
4515 Seton Center Parkway, Suite 320, Austin, TX, 78759
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
158034665
Principal Investigator:
Chris Augeri
Principal Investigator
(512) 342-0010
caugeri@21technologies.com
Business Contact:
Irene Williams
CEO
(512) 342-0010
SBIR_Admin@21technologies.com
Research Institution:
CUBRC
Kevin Leous
PO Box 400
4455 Genesee St
Buffalo, NY, 14225
(716) 204-5137
Nonprofit college or university
Abstract
21CT and CUBRC propose CEDAR (Complex Event Discovery, Analysis, and Ranking), a robust framework to detect and analyze indicators of complex activities, such as an insurgent ambush observable via myriad of simple events in multiple sensor streams. CEDAR will provide a mapping from complex behavior, such as conducting an insurgent ambush or rebuilding trust in local communities, to simple event indicators detectable in intelligence, open-source, blue force, and population sensor streams. Examples of such events include increased chatter in blue or red force networks, changes in population sentiment, or curfew movement. CEDAR will also provide an integrated process to execute event queries that leverages our team's abilities in performing approximate pattern matching over multi-dimensional data. This capability enables us to mine sensor feeds at scale, such as detecting motion in video, non-verbal audio cues, such as gunfire, sentiment in text sources, and changes in human network activity. Finally, CEDAR will provide a suite of event projections to facilitate discriminating aggregate events that indicate complex activity. By combining event projections with semantic event scoring, we can assess if detected events are progressing on a "good" or "bad" vector and exploit this information to choose actions to modulate complex event behavior.

* information listed above is at the time of submission.

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