Today's large and high-resolution displays coupled with powerful graphics hardware offer the potential for highly realistic 3D virtual environments, but also cause increased target acquisition difficulty for users interacting with these environments. We present an adaptation of semantic pointing to object picking in 3D environments. Essentially, semantic picking shrinks empty space and expands potential targets on the screen by dynamically adjusting the ratio between movement in visual space and motor space for relative input devices such as the mouse. Our implementation operates in the image-space using a hierarchical representation of the standard stencil buffer to allow for real-time calculation of the closest targets for all positions on the screen. An informal user study indicates that subjects perform more accurate pointing with semantic 3D pointing than without.

Interaction and navigation in large geometric spaces typically require a sequence of pan and zoom actions. This strategy is often ineffective and cumbersome, especially when trying to study several distant objects. We propose a new distortion technique that folds the intervening space to guarantee visibility of multiple focus regions. The folds themselves show contextual information and support unfolding and paging interactions. Compared to previous work, our method provides more context and distance awareness. We conducted a study comparing the space-folding technique to existing approaches, and found that participants performed significantly better with the new technique.

Motion constraints providing guidance for 3D navigation have recently been suggested as a way of offloading some of the cognitive effort of traversing complex 3D environments on a computer. We present findings from an evaluation of the benefits of this practice where users achieved significantly better results in memory recall and performance when given access to such a guidance method. The study was conducted on both standard desktop computers with mouse and keyboard, as well as on an immersive CAVE system. Interestingly, our results also show that the improvements were more dramatic for desktop users than for CAVE users, even outperforming the latter. Furthermore, the study indicates that allowing the users to retain local control over the navigation on the desktop platform helps them in familiarizing themselves with the 3D world.

We present the Zoomable Adjacency Matrix Explorer (ZAME), a visualization tool for exploring graphs at a scale of millions of nodes and edges. ZAME is based on an adjacency matrix graph representation aggregated at multiple scales. It allows analysts to explore a graph at many levels, zooming and panning with interactive performance from an overview to the most detailed views. Several components work together in the ZAME tool to make this possible. Efficient matrix ordering algorithms group related elements. Individual data cases are aggregated into higher-order meta representations. Aggregates are arranged into a pyramid hierarchy that allows for on-demand paging to GPU shader programs to support smooth multiscale browsing. Using ZAME, we are able to explore the entire French Wikipedia---over 500,000 articles and 6,000,000 links---with interactive performance on standard consumer-level computer hardware.

Scatterplots remain one of the most popular and widely-used visual representations for multidimensional data due to their simplicity, familiarity and visual clarity, even if they lack some of the flexibility and visual expressiveness of newer multidimensional visualization techniques. This paper presents new interactive methods to explore multidimensional data using scatterplots. This exploration is performed using a matrix of scatterplots that gives an overview of the possible configurations, thumbnails of the scatterplots, and support for interactive navigation in the multidimensional space. Transitions between scatterplots are performed as animated rotations in 3D space, somewhat akin to rolling dice. Users can iteratively build queries using bounding volumes in the dataset, sculpting the query from different viewpoints to become more and more refined. Furthermore, the dimensions in the navigation space can be reordered, manually or automatically, to highlight salient correlations and differences among them. An example scenario presents the interaction techniques supporting smooth and effortless visual exploration of multidimensional datasets.

While an important factor in depth perception, the occlusion effect in 3D environments also has a detrimental impact on tasks involving discovery, access, and spatial relation of objects in a 3D visualization. A number of interactive techniques have been developed in recent years to directly or indirectly deal with this problem using a wide range of different approaches. In this paper, we build on previous work on mapping out the problem space of 3D occlusion by defining a taxonomy of the design space of occlusion management techniques in an effort to formalize a common terminology and theoretical framework for this class of interactions. We classify a total of 50 different techniques for occlusion management using our taxonomy and then go on to analyze the results, deriving a set of five orthogonal design patterns for effective reduction of 3D occlusion. We also discuss the "gaps" in the design space, areas of the taxonomy not yet populated with existing techniques, and use these to suggest future research directions into occlusion management.

Supporting visual analytics of multiple large-scale multidimensional datasets requires a high degree of interactivity and user control beyond the conventional challenges of visualizing such datasets. We present the DataMeadow, a visual canvas providing rich interaction for constructing visual queries using graphical set representations called DataRoses. A DataRose is essentially a starplot of selected columns in a dataset displayed as multivariate visualizations with dynamic query sliders integrated into each axis. The purpose of the DataMeadow is to allow users to create advanced visual queries by iteratively selecting and filtering into the multidimensional data. Furthermore, the canvas provides a clear history of the analysis that can be annotated to facilitate dissemination of analytical results to stakeholders. A powerful direct manipulation interface allows for selection, filtering, and creation of sets, subsets, and data dependencies. We have evaluated our system using a qualitative expert review involving two visualization researchers. Results from this review are favorable for the new method.