Before revision:  We accomplish this by integrating an active search algorithm into the visual interface, which leads users to explore promising data points on a visualization and updates suggestions upon observing user feedback. Using a fictitious epidemic dataset published by the VAST community, we conduct two evaluations. First, we present simulation results that demonstrate the effectiveness of active search in data discovery. Second, we show how a human-computer partnership based on ActiveVA can result in more meaningful interactions during interactive visual exploration and discovery with a crowd-sourced user study.
After revision:  We accomplish this by integrating an active search algorithm that leads the user toward the points judged most promising for exploration. Using the epidemic dataset from VAST Challenge 2011, we design and conduct a crowd-sourced user study to evaluate several aspects of this technique. We present evidence that a human--computer partnership based on ActiveVA can result in more meaningful interactions during interactive visual exploration and discovery with a crowd-sourced user study.