Publications

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.

Many governmental and corporate organizations are interested in tracking materials and/or information through a network. Often, as in the case of the U.S. Customs Service, the traffic is recorded as transactions through a large number of checkpoints with a correspondingly complex network. These networks will contain large numbers of uninteresting transactions that act as noise to conceal the chains of transactions of interest, such as drug trafficking. We are interested in finding significant paths in transaction data containing high noise levels, which tend to make traditional graph visualization methods complex and hard to understand. This paper covers the evolution of a series of graphing methods designed to assist in this search for paths-from 1-D to 2-D to 3-D and beyond.