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Engineer Project Defense for Aaron Roy Newman
Project Defense for Engineer Degree
The Volgenau School of Information Technology & Engineering, George Mason University
Aaron Roy Newman
Bachelor of Science, 1992
Master of Science, 1993
*Confidence, Pedigree, and Security Classification for Improved Data Fusion*
Tuesday, November 10, 2009, 1:30pm – 3:30pm
Room 1602, Engineering Bldg.
All are invited to attend.
*_Committee_*
Kuo-Chu Chang, Project Director & Chair
Chun-Hung Chen
John Shortle
*__*
*_Abstract_*
Automated data fusion can reduce the burden on intelligence analysts who can be overwhelmed by available data. Automation can also reduce delivery time of intelligence products to users. Users of fused intelligence only see finished products, and have little insight into the sources that contributed to those products. Such insight is generally not needed or desired, if the users trust the underlying process. To gain that trust, automated data fusion systems require knowledge of the quality (confidence) and pedigree (source and heritage of previous processing) of all data processed. This project focuses on the design and prototype of a standard representation of confidence, pedigree, and security classification metadata, or CPSM. Current research is focused on solving issues of data overhead and throughput. The paper describes the need for and benefits of the standard, reviews the prototype, discusses results of performance tests, and introduces
concepts for future research.
The Volgenau School of Information Technology & Engineering, George Mason University
Aaron Roy Newman
Bachelor of Science, 1992
Master of Science, 1993
*Confidence, Pedigree, and Security Classification for Improved Data Fusion*
Tuesday, November 10, 2009, 1:30pm – 3:30pm
Room 1602, Engineering Bldg.
All are invited to attend.
*_Committee_*
Kuo-Chu Chang, Project Director & Chair
Chun-Hung Chen
John Shortle
*__*
*_Abstract_*
Automated data fusion can reduce the burden on intelligence analysts who can be overwhelmed by available data. Automation can also reduce delivery time of intelligence products to users. Users of fused intelligence only see finished products, and have little insight into the sources that contributed to those products. Such insight is generally not needed or desired, if the users trust the underlying process. To gain that trust, automated data fusion systems require knowledge of the quality (confidence) and pedigree (source and heritage of previous processing) of all data processed. This project focuses on the design and prototype of a standard representation of confidence, pedigree, and security classification metadata, or CPSM. Current research is focused on solving issues of data overhead and throughput. The paper describes the need for and benefits of the standard, reviews the prototype, discusses results of performance tests, and introduces
concepts for future research.
Posted on: October 27, 2009
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