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Adaptive Visualization of Social Networks (ADVIS)

Award Information
Agency: Department of Defense
Branch: Defense Advanced Research Projects Agency
Contract: W31P4Q-14-C-0048
Agency Tracking Number: D2-1209
Amount: $749,997.00
Phase: Phase II
Program: STTR
Solicitation Topic Code: ST12A-004
Solicitation Number: 2012.A
Timeline
Solicitation Year: 2012
Award Year: 2014
Award Start Date (Proposal Award Date): 2013-11-26
Award End Date (Contract End Date): 2016-02-28
Small Business Information
3527 Beverly Glen Blvd.
Sherman Oaks, CA -
United States
DUNS: 124668711
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Amos Freedy
 President
 (818) 788-4830
 afreedy@percsolutions.com
Business Contact
 Gershon Weltman
Title: Vice President
Phone: (818) 788-1025
Email: gweltman@percsolutions.com
Research Institution
 University of California Irvine
 Rick Grannis
 
3151 Social Science Plaza
Irvine, CA 92697-5100
United States

 (849) 824-6800
 Nonprofit College or University
Abstract

This proposal is to extend into Phase II our development of a new system for Adaptive Visualization of Social Networks (ADVIS). Recent years have seen a dramatic increase in the collection and study of social network data. Numerous social network visualizations have been developed to support user exploration and understanding of these data. However even in the few best systems, cognitive and design principles have been applied in ad hoc and non-systematic manner. The resulting visualization tools, while fine-tuned to a particular problem domain, cannot be adapted to changing user goals and priorities, data sets, and analyses. In our proposed approach, visualizations will instead be automatically generated in response to user-supplied tasks, based on a cognitive science framework that integrates"first principles"derived from the latest findings from perceptual and cognitive psychology, computer interface design, and cognitive systems engineering. The key innovation in our proposed system is an automated reasoning process that selects an optimal set of visualization techniques by explicitly considering the underlying cognitive premises of those techniques, and their interactions. As user goals, priorities, and data change, the system will continually create and re-create visualizations that are optimally tuned to most effectively support the user.

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

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