SHIELD (Novel Techniques for the Synthesis of High Fidelity Social Network Data)

Award Information
Agency: Department of Defense
Branch: Defense Advanced Research Projects Agency
Contract: W31P4Q-12-C-0223
Agency Tracking Number: D2-1152
Amount: $2,896,240.00
Phase: Phase II
Program: SBIR
Awards Year: 2012
Solicitation Year: 2011
Solicitation Topic Code: SB112-004
Solicitation Number: 2011.2
Small Business Information
Perceptronics Solutions, Inc.
3527 Beverly Glen Blvd., Sherman Oaks, CA, 91423
DUNS: 124668711
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Amos Freedy
 President
 (818) 788-4830
 afreedy@percsolutions.com
Business Contact
 Gershon Weltman
Title: Dr.
Phone: (818) 788-4830
Email: gweltman@percsolutions.com
Research Institution
 Stub
Abstract
This proposal is to extend into Phase II our development of Novel Techniques for the Synthesis of High Fidelity Social Network Data (SHIELD). Our objective in the SHIELD project is to develop scalable approaches, algorithms and tools to synthesize, validate and employ high fidelity social network data, and to provide these capabilities to researchers and analysts in an integrated, easy-to-use system. Our proposed Phase II effort will build on our significant technical achievements in Phase I, which include a successful Proof-of-Concept Demonstration. The large and growing interest in social network analysis has fueled increased demand from the research and intelligence communities for social network data. But while massive amounts of data are collected by military, government and commercial entities, very little of this data is available for research and analysis. In our innovative solution we generate synthetic data by pulling apart a real data dataset; we put it back together using a number of unique algorithmic methods; and we validate that our synthetic data corresponds to the original data in all key characteristics. Finally, we provide a user interface with advanced visualization tools so that researchers can interact naturally and easily with the algorithms and the synthetic network data.

* information listed above is at the time of submission.

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