Interest Driven Navigation in Visualization

ABSTRACT:

This paper describes a new method to explore and discover within a large dataset. We apply techniques from preference elicition to automatically identify data elements that are of potential interest to the viewer. These "elements of interest" are bundled into spatially local clusters, and connected together to form a graph. The graph is used to build camera paths that allow viewers to "tour" areas of interest  within their data. It is also visualized to provide wayfinding cues. Our preference model uses Bayesian classification to tag elements in a dataset as interesting or not interesting to the viewer. The model responds in real-time, updating the elements of interest based on a viewer's actions. This allows us to track a viewer's interests as they change during exploration and analysis. Viewers can also interact directly with interest rules the preference model defines. We demonstrate our theoretical results by visualizing historical climatology data collected at locations throughout the world.