We present Topic View, an application for visually comparing and exploring multiple models of text corpora. Topic View uses multiple linked views to visually analyze both the conceptual content and the document relationships in models generated using different algorithms. To illustrate Topic View, we apply it to models created using two standard approaches: Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA). Conceptual content is compared through the combination of (i) a bipartite graph matching LSA concepts with LDA topics based on the cosine similarities of model factors and (ii) a table containing the terms for each LSA concept and LDA topic listed in decreasing order of importance. Document relationships are examined through the combination of (i) side-by-side document similarity graphs, (ii) a table listing the weights for each document's contribution to each concept/topic, and (iii) a full text reader for documents selected in either of the graphs or the table. We demonstrate the utility of Topic View's visual approach to model assessment by comparing LSA and LDA models of two example corpora.

VisTrails is a new system that enables interactive multiple-view visualizations by simplifying the creation and maintenance of visualization pipelines, and by optimizing their execution. It provides a general infrastructure that can be combined with existing visualization systems and libraries. A key component of VisTrails is the visualization trail (vistrail), a formal specification of a pipeline. Unlike existing dataflow-based systems, in VisTrails there is a clear separation between the specification of a pipeline and its execution instances. This separation enables powerful scripting capabilities and provides a scalable mechanism for generating a large number of visualizations. VisTrails also leverages the vistrail specification to identify and avoid redundant operations. This optimization is especially useful while exploring multiple visualizations. When variations of the same pipeline need to be executed, substantial speedups can be obtained by caching the results of overlapping subsequences of the pipelines. In this paper, we describe the design and implementation of VisTrails, and show its effectiveness in different application scenarios.

Huge salt formations, trapping large untapped oil and gas reservoirs, lie in the deepwater region of the Gulf of Mexico. Drilling in this region is high-risk and drilling failures have led to well abandonments, with each costing tens of millions of dollars. Salt tectonics plays a central role in these failures. To explore the geomechanical interactions between salt and the surrounding sand and shale formations, scientists have simulated the stresses in and around salt diapirs in the Gulf of Mexico using nonlinear finite element geomechanical modeling. In this paper, we describe novel techniques developed to visualize the simulated subsurface stress field. We present an adaptation of the Mohr diagram, a traditional paper-and-pencil graphical method long used by the material mechanics community for estimating coordinate transformations for stress tensors, as a new tensor glyph for dynamically exploring tensor variables within three-dimensional finite element models. This interactive glyph can be used as either a probe or a filter through brushing and linking.

An important challenge encountered during post-processing of finite element analyses is the visualizing of three-dimensional fields of real-valued second-order tensors. Namely, as finite element meshes become more complex and detailed, evaluation and presentation of the principal stresses becomes correspondingly problematic. In this paper, we describe techniques used to visualize simulations of perturbed in-situ stress fields associated with hypothetical salt bodies in the Gulf of Mexico. We present an adaptation of the Mohr diagram, a graphical paper and pencil method used by the material mechanics community for estimating coordinate transformations for stress tensors, as a new tensor glyph for dynamically exploring tensor variables within three-dimensional finite element models. This interactive glyph can be used as either a probe or a filter through brushing and linking.

We present an innovative application developed at Sandia National Laboratories for visual debugging of unstructured finite element physics codes. Our tool automatically locates anomalous regions, such as inverted elements or nodes whose variable values lie outside a prescribed range, then extracts mesh subsets around these features for detailed examination. The subsets are viewed using color coding of variable values superimposed on the mesh structure. This allows the values and their relative spatial locations within the mesh to be correlated at a glance. Both topological irregularities and hot spots within the data stand out visually, allowing the user to explore the exact numeric values of the grid at surrounding points over time. We demonstrate the utility of this approach by debugging a cell inversion in a simulation of an exploding wire.

This paper describes a unique visualization tool that has been used to analyze performance of the Cplant(tm) clusters at Sandia National Laboratories.As commodity cluster systems grow in size and complexity, understanding performance issues becomes more and more difficult.We have developed a tool that facilitates visual performance analysis within the context of the physical and runtime environment of a system. Combining an abstract system model with color-coding for both performance and job information enables quick fault isolation as well as insight into complex system behavior.

This paper presents a novel use of visualization applied to debugging the Cplant/sup TM/ cluster hardware at Sandia National Laboratories. As commodity cluster systems grow in popularity and grow in size, tracking component failures within the hardware will become more and more difficult. We have developed a tool that facilitates visual debugging of errors within the switches and cables connecting the processors. Combining an abstract system model with color-coding for both error and job information enables failing components to be identified.

Many applications produce three-dimensional points that must be further processed to generate a surface. Surface reconstruction algorithms that start with a set of unorganized points are extremely time-consuming. Sometimes however, points are generated such that there is additional information available to the reconstruction algorithm. We present Spiraling Edge, a specialized algorithm for surface reconstruction that is three orders of magnitude faster than algorithms for the general case. In addition to sample point locations, our algorithm starts with normal information and knowledge of each point's neighbors. Our algorithm produces a localized approximation to the surface by creating a star-shaped triangulation between a point and a subset of its nearest neighbors. This surface patch is extended by locally triangulating each of the points along the edge of the patch. As each edge point is triangulated, it is removed from the edge and new edge points along the patch's edge are inserted in its place. The updated edge spirals out over the surface until the edge encounters a surface boundary and stops growing in that direction, or until the edge reduces to a small hole that is filled by the final triangle.

Visualization systems are complex dynamic software systems. Debugging such systems is difficult using conventional debuggers because the programmer must try to imagine the three-dimensional geometry based on a list of positions and attributes. In addition, the programmer must be able to mentally animate changes in those positions and attributes to grasp dynamic behaviors within the algorithm. We show that representing geometry, attributes, and relationships graphically permits visual pattern recognition skills to be applied to the debugging problem. The particular application is a particle system used for isosurface extraction from volumetric data. Coloring particles based on individual attributes is especially helpful when these colorings are viewed as animations over successive iterations in the program. Although we describe a particular application, the types of tools that we discuss can be applied to a variety of problems.

Presents a new approach to isosurface extraction from volume data using particle systems. Particle behavior is dynamic and can be based on laws of physics or artificial rules. For isosurface extraction, we program particles to be attracted towards a specific surface value while simultaneously repelling adjacent particles. The repulsive forces are based on the curvature of the surface at that location. A birth-death process results in a denser concentration of particles in areas of high curvature and sparser populations in areas of lower curvature. The overall level of detail is controlled through a scaling factor that increases or decreases the repulsive forces of the particles. Once particles reach equilibrium, their locations are used as vertices in generating a triangular mesh of the surface. The advantages of our approach include: vertex densities are based on surface features rather than on the sampling rate of the volume; a single scaling factor simplifies level-of-detail control; and meshing is efficient because it uses neighbor information that has already been generated during the force calculations.

In researching the communication mechanisms between cells of the immune system, visualization of proteins in three dimensions can be used to determine which proteins are capable of interacting with one another at a given time by showing their spatial colocality. Volume data sets are created using digital confocal immunofluorescence microscopy. A variety of visualization approaches are then used to examine the interactions. These include volume rendering, isosurface extraction, and virtual reality. Based on our experiences, we have concluded that no single one of these approaches provides a complete solution for visualizing biological data. However, in combination, their respective strengths complement one another to provide an understanding of the data.