Kristin Potter

Ensemble-Vis: A Framework for the Statistical Visualization of Ensemble Data

Scientists increasingly use ensemble data sets to explore relationships present in dynamic systems. Ensemble data sets combine spatio-temporal simulation results generated using multiple numerical models, sampled input conditions and perturbed parameters. While ensemble data sets are a powerful tool for mitigating uncertainty, they pose significant visualization and analysis challenges due to their complexity. In this article, we present Ensemble-Vis, a framework consisting of a collection of overview and statistical displays linked through a high level of interactivity. Ensemble-Vis allows scientists to gain key scientific insight into the distribution of simulation results as well as the uncertainty associated with the scientific data. In contrast to methods that present large amounts of diverse information in a single display, we argue that combining multiple linked displays yields a clearer presentation of the data and facilitates a greater level of visual data analysis. We demonstrate our framework using driving problems from climate modeling and meteorology and discuss generalizations to other fields.

A user study of visualization effectiveness using EEG and cognitive load

Effectively evaluating visualization techniques is a difficult task often assessed through feedback from user studies and expert evaluations. This work presents an alternative approach to visualization evaluation in which brain activity is passively recorded using electroencephalography (EEG). These measurements are used to compare different visualization techniques in terms of the burden they place on a viewers cognitive resources. In this paper, EEG signals and response times are recorded while users interpret different representations of data distributions. This information is processed to provide insight into the cognitive load imposed on the viewer. This paper describes the design of the user study performed, the extraction of cognitive load measures from EEG data, and how those measures are used to quantitatively evaluate the effectiveness of visualizations.

From Quantification to Visualization: A Taxonomy of Uncertainty Visualization Approaches

Quantifying uncertainty is an increasingly important topic across many domains. The uncertainties present in data come with many diverse representations having originated from a wide variety of disciplines. Communicating these uncertainties is a task often left to visualization without clear connection between the quantification and visualization. In this paper, we first identify frequently occurring types of uncertainty. Second, we connect those uncertainty representations to ones commonly used in visualization. We then look at various approaches to visualizing this uncertainty by partitioning the work based on the dimensionality of the data and the dimensionality of the uncertainty. We also discuss noteworthy exceptions to our taxonomy along with future research directions for the uncertainty visualization community.

Visualizing summary statistics and uncertainty

The graphical depiction of uncertainty information is emerging as a problem of great importance. Scientific data sets are not considered complete without indications of error, accuracy, or levels of confidence. The visual portrayal of this information is a challenging task. This work takes inspiration from graphical data analysis to create visual representations that show not only the data value, but also important characteristics of the data including uncertainty. The canonical box plot is reexamined and a new hybrid summary plot is presented that incorporates a collection of descriptive statistics to highlight salient features of the data. Additionally, we present an extension of the summary plot to two dimensional distributions. Finally, a use‐case of these new plots is presented, demonstrating their ability to present high‐level overviews as well as detailed insight into the salient features of the underlying data distribution.

Overview and State-of-the-Art of Uncertainty Visualization

The goal of visualization is to effectively and accurately communicate data. Visualization research has often overlooked the errors and uncertainty which accompany the scientific process and describe key characteristics used to fully understand the data. The lack of these representations can be attributed, in part, to the inherent difficulty in defining, characterizing, and controlling this uncertainty, and in part, to the difficulty in including additional visual metaphors in a well designed, potent display. However, the exclusion of this information cripples the use of visualization as a decision making tool due to the fact that the display is no longer a true representation of the data. This systematic omission of uncertainty commands fundamental research within the visualization community to address, integrate, and expect uncertainty information. In this chapter, we outline sources and models of uncertainty, give an overview of the state-of-the-art, provide general guidelines, outline small exemplary applications, and finally, discuss open problems in uncertainty visualization.

Visualization of uncertainty and ensemble data: Exploration of climate modeling and weather forecast data with integrated ViSUS-CDAT systems

Climate scientists and meteorologists are working towards a better understanding of atmospheric conditions and global climate change. To explore the relationships present in numerical predictions of the atmosphere, ensemble datasets are produced that combine time- and spatially-varying simulations generated using multiple numeric models, sampled input conditions, and perturbed parameters. These data sets mitigate as well as describe the uncertainty present in the data by providing insight into the effects of parameter perturbation, sensitivity to initial conditions, and inconsistencies in model outcomes. As such, massive amounts of data are produced, creating challenges both in data analysis and in visualization. This work presents an approach to understanding ensembles by using a collection of statistical descriptors to summarize the data, and displaying these descriptors using variety of visualization techniques which are familiar to domain experts. The resulting techniques are integrated into the ViSUS/Climate Data and Analysis Tools (CDAT) system designed to provide a directly accessible, complex visualization framework to atmospheric researchers.

Ray Bilinear Patch Intersections

Abstract Ray tracing and other techniques employ algorithms which require the intersection between a three-dimensional parametric ray and an object to be computed. The object to intersect is typically a sphere, triangle, or polygon but many surface types are possible. In this work we consider intersections between rays and the simplest parametric surface, the bilinear patch. Unlike other surfaces, solving the ray-bilinear patch intersection with simple algebraic manipulations fails. We present a complete, efficient, robust, and graceful formulation to solve ray-bilinear patch intersections quickly. Source code is available online.

Toward visual analysis of ensemble data sets

The rapid and continuing increase in available high-performance computing resources has driven simulation-based science in two directions. First, the simulations themselves are growing more complex, whether in the fidelity of the models, spatiotemporal resolution or (more frequently) both. Second, multiple instances of a simulation can be run to sample the results of parameters within a given space instead of at a single point. We name the results of such a family of runs an ensemble data set. In this paper we discuss the properties of ensemble data sets, consider their implications for analysis and visualization algorithms, and present a few insights into promising avenues of investigation.

Interactive visualization for memory reference traces

We present the Memory Trace Visualizer (MTV), a tool that provides interactive visualization and analysis of the sequence of memory operations performed by a program as it runs. As improvements in processor performance continue to outpace improvements in memory performance, tools to understand memory access patterns are increasingly important for optimizing data intensive programs such as those found in scientific computing. Using visual representations of abstract data structures, a simulated cache, and animating memory operations, MTV can expose memory performance bottlenecks and guide programmers toward memory system optimization opportunities. Visualization of detailed memory operations provides a powerful and intuitive way to expose patterns and discover bottlenecks, and is an important addition to existing statistical performance measurements.

Height field haptics

We present a system for haptically rendering large height field datasets. In as much as, height fields are naturally mapped to piecewise bilinear patches. We develop algorithms for intersection, penetration depth, and closest point tracking using bilinear patches. In contrast to many common haptic rendering schemes for polygonal models, this approach does not require preprocessing or additional storage. Thus, it is particularly suitable for the large scale datasets found in geographic and reverse engineering applications.