Charles Hansen

Data analysis often involves the comparison of complex objects. With the ever increasing amounts and complexity of data, the demand for systems to help with these comparisons is also growing. Increasingly, information visualization tools support such comparisons explicitly, beyond simply allowing a viewer to examine each object individually. In this paper, we argue that the design of information visualizations of complex objects can, and should, be studied in general, that is independently of what those objects are. As a first step in developing this general understanding of comparison, we propose a general taxonomy of visual designs for comparison that groups designs into three basic categories, which can be combined. To clarify the taxonomy and validate its completeness, we provide a survey of work in information visualization related to comparison. Although we find a great diversity of systems and approaches, we see that all designs are assembled from the building blocks of juxtaposition, superposition and explicit encodings. This initial exploration shows the power of our model, and suggests future challenges in developing a general understanding of comparative visualization and facilitating the development of more comparative visualization tools.

Most direct volume renderings produced today employ 1D transfer functions which assign color and opacity to the volume based solely on the single scalar quantity which comprises the data set. Though they have not received widespread attention, multi-dimensional transfer functions are a very effective way to extract materials and their boundaries for both scalar and multivariate data. However, identifying good transfer functions is difficult enough in 1D, let alone 2D or 3D. This paper demonstrates an important class of 3D transfer functions for scalar data, and describes the application of multi-dimensional transfer functions to multivariate data. We present a set of direct manipulation widgets that make specifying such transfer functions intuitive and convenient. We also describe how to use modern graphics hardware to both interactively render with multidimensional transfer functions and to provide interactive shadows for volumes. The transfer functions, widgets and hardware combine to form a powerful system for interactive volume exploration.

We describe a parallel volume-rendering algorithm, which consists of two parts: parallel ray tracing and parallel compositing. In the most recent implementation on Connection Machine's CM-5 and networked workstations, the parallel volume renderer evenly distributes data to the computing resources available. Without the need to communicate with other processing units, each subvolume is ray traced locally and generates a partial image. The parallel compositing process then merges all resulting partial images in depth order to produce the complete image. The compositing algorithm is particularly effective for massively parallel processing, as it always uses all processing units by repeatedly subdividing the partial images and distributing them to the appropriate processing units. Test results on both the CM-5 and the workstations are promising. They do, however, expose different performance issues for each platform.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>