Patricia Crossno

VisTrails: enabling interactive multiple-view visualizations

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.

Tetrahedral projection using vertex shaders

Projective methods for volume rendering currently represent the best approach for interactive visualization of unstructured data sets. We present a technique for tetrahedral projection using the programmable vertex shaders on current generation commodity graphics cards. The technique is based on Shirley and Tuchmans Projected Tetrahedra (PT) algorithm and allows tetrahedral elements to be volume scan converted within the graphics processing unit. Our technique requires no pre-processing of the data and no additional data structures. Our initial implementation allows interactive viewing of large unstructured datasets on a desktop personal computer.

Isosurface extraction using particle systems

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.

A visualization tool for analyzing cluster performance data

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.

Spiraling edge: fast surface reconstruction from partially organized sample points

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 points 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 patchs 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.

Three dimensional visualization of proteins in cellular interactions

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.

LSAView: A tool for visual exploration of latent semantic modeling

Latent Semantic Analysis (LSA) is a commonly-used method for automated processing, modeling, and analysis of unstructured text data. One of the biggest challenges in using LSA is determining the appropriate model parameters to use for different data domains and types of analyses. Although automated methods have been developed to make rank and scaling parameter choices, these approaches often make choices with respect to noise in the data, without an understanding of how those choices impact analysis and problem solving. Further, no tools currently exist to explore the relationships between an LSA model and analysis methods. Our work focuses on how parameter choices impact analysis and problem solving. In this paper, we present LSAView, a system for interactively exploring parameter choices for LSA models. We illustrate the use of LSAViews small multiple views, linked matrix-graph views, and data views to analyze parameter selection and application in the context of graph layout and clustering.

Visual debugging of visualization software: a case study for particle systems

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.

TopicView: Visually Comparing Topic Models of Text Collections

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 documents 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 Views visual approach to model assessment by comparing LSA and LDA models of two example corpora.

Comparison of Open Source Visual Analytics Toolkits.

We present the results of the first stage of a two-stage evaluation of open source visual analytics packages. This stage is a broad feature comparison over a range of open source toolkits. Although we had originally intended to restrict ourselves to comparing visual analytics toolkits, we quickly found that very few were available. So we expanded our study to include information visualization, graph analysis, and statistical packages. We examine three aspects of each toolkit: visualization functions, analysis capabilities, and development environments. With respect to development environments, we look at platforms, language bindings, multi-threading/parallelism, user interface frameworks, ease of installation, documentation, and whether the package is still being actively developed.