When Leilani Battle was working on her PhD, she helped develop ForeCache, a tool designed to help researchers browse large arrays of data—for instance, scanning high-resolution satellite images to look for areas covered with snow. The goal is to reduce latency, so that a user can pan and zoom across the data set without perceptible delay. A common way to do this is to predict which parts of the data a user is likely to need and then “prefetch” them. But how to predict what to prefetch? That depends on understanding the user’s behavior.
Battle and her colleagues developed a more efficient prediction system. It attempts to discern first which “analysis phase” a user is in, and then what tiles of data might be wanted next. They dubbed the three phrases “foraging,” “sensemaking,” and “navigation.” They suppose that users in the “foraging” phase are browsing at a coarse level, in order to come up with new ideas. “Sensemaking” is a closer examination meant to test those ideas, and “navigation” is a transition between the two.
This system allowed them, they said, to predict which tiles users wanted about 25% better than existing prefetching systems they benchmarked against, almost halving the latency.
Battle has devoted her career to designing systems and interfaces that help researchers sifting through data do their work better and faster. She hopes to make exploration tools more interactive and visual so they’ll be less daunting. Perhaps this will allow scientists to spot data quirks that would otherwise go unnoticed.