The Design of RenderMan.

The design of RenderMan was driven by the requirements of rendering for the movies. The rendered images could have no digital artifacts and they had to be able to be composited seamlessly with live-action footage. This article recounts the development of RenderMan. It tells the story of the invention of the fundamental architecture and algorithms, the application of good engineering and design principles, RenderMan's use in feature films, the long-term influence of the RenderMan Shading Language on real-time graphics and high-performance computing, and, where the name came from.

Arc length-based aspect ratio selection.

The aspect ratio of a plot has a dramatic impact on our ability to perceive trends and patterns in the data. Previous approaches for automatically selecting the aspect ratio have been based on adjusting the orientations or angles of the line segments in the plot. In contrast, we recommend a simple, effective method for selecting the aspect ratio: minimize the arc length of the data curve while keeping the area of the plot constant. The approach is parameterization invariant, robust to a wide range of inputs, preserves visual symmetries in the data, and is a compromise between previously proposed techniques. Further, we demonstrate that it can be effectively used to select the aspect ratio of contour plots. We believe arc length should become the default aspect ratio selection method.

Effects of visual and verbal presentation on cognitive load in vigilance, memory, and arithmetic tasks.

Degree of pupil dilation has been shown to be a valid and reliable measure of cognitive load, but the effect of aural versus visual task presentation on pupil dilation is unknown. To evaluate effects of presentation mode, pupil dilation was measured in three tasks spanning a range of cognitive activities: mental multiplication, digit sequence recall, and vigilance. Stimuli were presented both aurally and visually, controlling for all known visual influences on pupil diameter. The patterns of dilation were similar for both aural and visual presentation for all three tasks, but the magnitudes of pupil response were greater for aural presentation. Accuracy was higher for visual presentation for mental arithmetic and digit recall. The findings can be accounted for in terms of dual codes in working memory and suggest that cognitive load is lower for visual than for aural presentation.

An extension of Wilkinson's algorithm for positioning tick labels on axes.

The non-data components of a visualization, such as axes and legends, can often be just as important as the data itself. They provide contextual information essential to interpreting the data. In this paper, we describe an automated system for choosing positions and labels for axis tick marks. Our system extends Wilkinson’s optimization-based labeling approach to create a more robust, full-featured axis labeler. We define an expanded space of axis labelings by automatically generating additional nice numbers as needed and by permitting the extreme labels to occur inside the data range. These changes provide flexibility in problematic cases, without degrading quality elsewhere. We also propose an additional optimization criterion, legibility, which allows us to simultaneously optimize over label formatting, font size, and orientation. To solve this revised optimization problem, we describe the optimization function and an efficient search algorithm. Finally, we compare our method to previous work using both quantitative and qualitative metrics. This paper is a good example of how ideas from automated graphic design can be applied to information visualization.

Vispedia: interactive visual exploration of wikipedia data via search-based integration.

Wikipedia is an example of the collaborative, semi-structured data sets emerging on the Web. These data sets have large, non-uniform schema that require costly data integration into structured tables before visualization can begin. We present Vispedia, a Web-based visualization system that reduces the cost of this data integration. Users can browse Wikipedia, select an interesting data table, then use a search interface to discover, integrate, and visualize additional columns of data drawn from multiple Wikipedia articles. This interaction is supported by a fast path search algorithm over DBpedia, a semantic graph extracted from Wikipedia's hyperlink structure. Vispedia can also export the augmented data tables produced for use in traditional visualization systems. We believe that these techniques begin to address the "long tail" of visualization by allowing a wider audience to visualize a broader class of data. We evaluated this system in a first-use formative lab study. Study participants were able to quickly create effective visualizations for a diverse set of domains, performing data integration as needed.

Show me: automatic presentation for visual analysis.

This paper describes Show Me, an integrated set of user interface commands and defaults that incorporate automatic presentation into a commercial visual analysis system called Tableau. A key aspect of Tableau is VizQL, a language for specifying views, which is used by Show Me to extend automatic presentation to the generation of tables of views (commonly called small multiple displays). A key research issue for the commercial application of automatic presentation is the user experience, which must support the flow of visual analysis. User experience has not been the focus of previous research on automatic presentation. The Show Me user experience includes the automatic selection of mark types, a command to add a single field to a view, and a pair of commands to build views for multiple fields. Although the use of these defaults and commands is optional, user interface logs indicate that Show Me is used by commercial users.

Visualization of heterogeneous data.

Both the Resource Description Framework (RDF), used in the semantic web, and Maya Viz u-forms represent data as a graph of objects connected by labeled edges. Existing systems for flexible visualization of this kind of data require manual specification of the possible visualization roles for each data attribute. When the schema is large and unfamiliar, this requirement inhibits exploratory visualization by requiring a costly up-front data integration step. To eliminate this step, we propose an automatic technique for mapping data attributes to visualization attributes. We formulate this as a schema matching problem, finding appropriate paths in the data model for each required visualization attribute in a visualization template.