Shortly before 911, suspects on watch lists moved money in curious ways. Internet and phone “chatter” had risen in recent months. A foreigner paid cash to learn how to fly?but not land?a jetliner. How did our government ever miss these clues?
Researcher Joseph Kielman thinks the problem is that we don’t look for patterns. Most nuggets of information about 911 were buried in a landslide of data arriving faster than analysts could make sense of it, and these nuggets sometimes contradicted each other.
Most nuggets of information come to us in unstructured, “fuzzy” data. The same face?or is it??may appear in three surveillance videos. Someone in Florida is snapping up potential makeshift detonators on eBay. Such clues don?t come conveniently packaged in a tidy spreadsheet or searchable text; they must be inferred from photos, videos, voice recordings. So how can we prevent the NEXT 911?
At the National Visualization and Analytics Center, mathematicians, logicians, and linguists are trying to make the collective universe of data assume a meaningful shape. They assign brightness, color, texture, and size to billions of known and apparent facts, and they create rules to integrate these values so threats stand out. For example, a day?s cache of video, cell phone calls, photos, bank records, chat rooms, and intercepted emails may take shape as a blue-gray cloud on a US map. If there seems to be a terror threat to a specific city, that city is highlighted.
“We’re not looking for ‘meaning,’ per se,” Kielman explains, “but for patterns that will let us detect the expected and discover the unexpected.” Neither the researchers nor the analysts need to understand the terrorists? language, which is good, given the shortage of cleared linguists. Nevertheless, it will be years before visual analytics can automatically puzzle out clues from fuzzy data like video.
Kielman reminds us that “the pre-9/11 chatter didn’t say, ‘We’re going to plow airplanes into the Twin Towers.'”
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