A toolkit for detecting technical surprise.

Trahan, Michael Wayne ; Foehse, Mark C.

Sandia National Laboratories

关键词: 99 General And Miscellaneous//Mathematics, Computing, And Information Science;    National Security;    Economics;    Technology Assessment;    Detection;   

DOI  :  10.2172/1011203
RP-ID  :  SAND2010-7392
RP-ID  :  AC04-94AL85000
RP-ID  :  1011203

美国|英语

来源: UNT Digital Library

【 摘 要 】

The detection of a scientific or technological surprise within a secretive country or institute is very difficult. The ability to detect such surprises would allow analysts to identify the capabilities that could be a military or economic threat to national security. Sandia's current approach utilizing ThreatView has been successful in revealing potential technological surprises. However, as data sets become larger, it becomes critical to use algorithms as filters along with the visualization environments. Our two-year LDRD had two primary goals. First, we developed a tool, a Self-Organizing Map (SOM), to extend ThreatView and improve our understanding of the issues involved in working with textual data sets. Second, we developed a toolkit for detecting indicators of technical surprise in textual data sets. Our toolkit has been successfully used to perform technology assessments for the Science & Technology Intelligence (S&TI) program.