AIDEN: Analysis of Intelligence Data for Evidence of Networks

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N00014-10-M-0455
Agency Tracking Number: N102-180-0167
Amount: $69,975.00
Phase: Phase I
Program: SBIR
Awards Year: 2011
Solicitation Year: 2010
Solicitation Topic Code: N102-180
Solicitation Number: 2010.2
Small Business Information
6011 West Courtyard Drive, Bldg 5, Suite 300, Austin, TX, -
DUNS: 158034665
HUBZone Owned: N
Woman Owned: Y
Socially and Economically Disadvantaged: Y
Principal Investigator
 Matthew McClain
 Principal Investigator
 (512) 682-4735
 mmcclain@21technologies.com
Business Contact
 Irene Williams
Title: CEO
Phone: (512) 682-4700
Email: SBIR_Admin@21technologies.com
Research Institution
 Stub
Abstract
The goal of intelligence, surveillance, and reconnaissance (ISR) operations is to provide analysts and warfighters with situational awareness information. ISR enterprise systems store data to support these operations, but large volumes make it difficult to locate information of interest, such as key individuals. Current keyword-search approaches fall short because they fail to capture semantics. Social network analysis (SNA) techniques, including group detection and SNA metric-based classification, have been demonstrated to find valuable information in graphs, such as networks of interest and the roles of individuals in those networks. Current natural language processing (NLP) tools extract entities, themes, and relationships from unstructured text. By combining such NLP tools, information in text-based documents can be converted into a graph. The application of SNA techniques to such graphs has the potential to enable analysts and warfighters to efficiently locate actionable intelligence in ISR enterprise datastores. 21st Century Technologies (21CT) proposes Analysis of Unstructured Data for Evidence of Networks (AIDEN), a tool for locating information of interest in large, diverse data sources. Our Phase 1 work will focus on a proof-of-concept for applying SNA on graphs obtained from NLP, and identification of gaps in state-of-the-art NLP that will need to be overcome.

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

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