A major part of ADVISE involves data-mining - or "dataveillance," as some call it. It means sifting through data to look for patterns. If a supermarket finds that customers who buy cider also tend to buy fresh-baked bread, it might group the two together. To prevent fraud, credit-card issuers use data-mining to look for patterns of suspicious activity.
What sets ADVISE apart is its scope. It would collect a vast array of corporate and public online information - from financial records to CNN news stories - and cross-reference it against US intelligence and law-enforcement records. The system would then store it as "entities" - linked data about people, places, things, organizations, and events, according to a report summarizing a 2004 DHS conference in Alexandria, Va. The storage requirements alone are huge - enough to retain information about 1 quadrillion entities, the report estimated. If each entity were a penny, they would collectively form a cube a half-mile high - roughly double the height of the Empire State Building.
But ADVISE and related DHS technologies aim to do much more, according to Joseph Kielman, manager of the TVTA portfolio. The key is not merely to identify terrorists, or sift for key words, but to identify critical patterns in data that illumine their motives and intentions, he wrote in a presentation at a November conference in Richland, Wash.
For example: Is a burst of Internet traffic between a few people the plotting of terrorists, or just bloggers arguing? ADVISE algorithms would try to determine that before flagging the data pattern for a human analyst's review.
At least a few pieces of ADVISE are already operational. Consider Starlight, which along with other "visualization" software tools can give human analysts a graphical view of data. Viewing data in this way could reveal patterns not obvious in text or number form. Understanding the relationships among people, organizations, places, and things - using social-behavior analysis and other techniques - is essential to going beyond mere data-mining to comprehensive "knowledge discovery in databases," Dr. Kielman wrote in his November report. He declined to be interviewed for this article.