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ACCESS: Automated Comparison and Clustering of Entity Signatures

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N00014-09-M-0439
Agency Tracking Number: N092-149-0133
Amount: $99,988.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: N092-149
Solicitation Number: 2009.2
Timeline
Solicitation Year: 2009
Award Year: 2010
Award Start Date (Proposal Award Date): 2009-10-26
Award End Date (Contract End Date): 2010-11-30
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
 Sandeep Parikh
 Principal Investigator
 (512) 342-0010
 sparikh@21technologies.com
Business Contact
 Irene Williams
Title: CEO
Phone: (512) 342-0010
Email: SBIR_ADMIN@21technologies.com
Research Institution
N/A
Abstract

21st Century Technologies’ (21CT) ACCESS (Automated Comparison and Clustering of Entity SignatureS) research effort addresses the issue of comparing entities such as human personas and networks, so that a more complete assessment of at-risk entities can be made within and across the various domains in which those entities interact. The Phase I effort of ACCESS will provide an effective similarity measure and clustering algorithm for the automated comparison of human signatures that arise within and across modalities and mediums so that personas of interest can be discovered among large databases of gathered intelligence. This similarity measure will be used to create higher fidelity personas and provide the means to detect asymmetric actors/at-risk entities that manifest behavior types of interest. Key components of the ACCESS Phase I effort include: 1) Identification of one or more useful similarity measures for clustering personas, 2) Experimental data to inform assessments of the effectiveness of the identified measures, and 3) Preliminary results identifying at-risk signatures. This builds directly upon existing 21CT technologies that generate signatures from raw data, providing the customer with the ability to determine signature similarity for the identification of persons of interest.

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

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