Abstract-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.

I nterest in visualization has grown in recent years, producing rapid advances in the diversity of research and in the scope of proposed techniques. Much of the initial focus in computer-based visualization concentrated on display algorithms, often for specific domains. For example, volume, flow, and terrain visualization techniques have generated significant insights into fundamental graphics and visualization theory, aiding the application experts who use these techniques to advance their own research. More recent work has extended visualization to abstract data sets like network intrusion detection, recommender systems, and database query results. Although display algorithms are a critical component in the visualization process, they are not the only issue to consider. More and more, we see visualization as a path from data to understanding. From this perspective, two obvious questions arise: ■ What should we do before we display the data? ■ What can we do after the user views the data? This is not a new idea, of course. Our work is motivated by others in the community, including methods to integrate data management into visualization, metadata generation and management, techniques to preprocess data to extract and display critical details, and intelligent systems that help users design effective visu-alizations. This article describes our initial end-to-end system that starts with data management and continues through assisted visualization design, display, navigation, and user interaction (see Figure 1). The purposes of this discussion are to ■ promote a more comprehensive visualization framework; ■ describe how to apply expertise from human psychophysics, databases, rational logic, and artificial intelligence to visualization; and ■ illustrate the benefits of a more complete framework using examples from our own experiences.

This paper describes a new technique to visualize 2D flow fields with a sparse collection of dots. A cognitive model proposed by Kent Stevens describes how spatially local configurations of dots are processed in parallel by the low-level visual system to perceive orientations throughout the image. We integrate this model into a visualization algorithm that converts a sparse grid of dots into patterns that capture flow orientations in an underlying flow field. We describe how our algorithm supports large flow fields that exceed the capabilities of a display device, and demonstrate how to include properties like direction and velocity in our visualizations. We conclude by applying our technique to 2D slices from a simulated supernova collapse.