This paper describes a new method to explore and discover within a large data set. We apply techniques from preference elicitation to automatically identify data elements that are of potential interest to the viewer. These "elements of interest (EOI)" 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 (AOI) within their data. It is also visualized to provide wayfinding cues. Our preference model uses Bayesian classification to tag elements in a data set 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.

This paper presents a new technique for visualizing large, complex collections of data. The size and dimensionality of these datasets make them challenging to display in an effective manner. The images must show the global structure of spatial relationships within the dataset, yet at the same time accurately represent the local detail of each data element being visualized. We propose combining ideas from information and scientific visualization together with a navigation assistant, a software system designed to help users identify and explore areas of interest within their data. The assistant locates data elements of potential importance to the user, clusters them into spatial regions, and builds underlying graph structures to connect the regions and the elements they contain. Graph traversal algorithms, constraint-based viewpoint construction, and intelligent camera planning techniques can then be used to design animated tours of these regions. In this way, the navigation assistant can help users to explore any of the areas of interest within their data. We conclude by demonstrating how our assistant is being used to visualize a multidimensional weather dataset.