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CEDAR (Complex Event Discovery, Analysis, and Ranking)

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N00014-10-M-0288
Agency Tracking Number: N10A-040-0061
Amount: $99,992.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: N10A-T040
Solicitation Number: 2010.A
Solicitation Year: 2010
Award Year: 2010
Award Start Date (Proposal Award Date): 2010-06-28
Award End Date (Contract End Date): 2011-08-30
Small Business Information
4515 Seton Center Parkway Suite 320
Austin, TX 78759
United States
DUNS: 158034665
HUBZone Owned: No
Woman Owned: Yes
Socially and Economically Disadvantaged: Yes
Principal Investigator
 Chris Augeri
 Principal Investigator
 (512) 342-0010
Business Contact
 Irene Williams
Title: CEO
Phone: (512) 342-0010
Research Institution
 Kevin Leous
PO Box 400 4455 Genesee St
Buffalo, NY 14225
United States

 (716) 204-5137
 Nonprofit College or University

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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