IEEE VIS Publication Dataset

Vorticity is the quantity used to describe the creation, transformation and extinction of vortices. It is present not only in vortices but also in shear flow. Especially in ducted flows, most of the overall vorticity is usually contained in the boundary layer. When a vortex develops from the boundary layer, this can be described by transport of vorticity. For a better understanding of a flow it is therefore of interest to examine vorticity in all of its different roles. The goal of this application study was not primarily the visualization of vortices but of vorticity distribution and its role in vortex phenomena. The underlying industrial case is a design optimization for a Pelton turbine. An important industrial objective is to improve the quality of the water jets driving the runner. Jet quality is affected mostly by vortices originating in the distributor ring. For a better understanding of this interrelation, it is crucial to not only visualize these vortices but also to analyze the mechanisms of their creation. We used various techniques for the visualization of vorticity, including field lines and modified isosurfaces. For field line based visualization, we extended the image-guided streamline placement algorithm of Turk and Banks to data-guided field line placement on three-dimensional unstructured grids.

We propose a new method for assessing the perceptual organization of information graphics, based on the premise that the visual structure of an image should match the structure of the data it is intended to convey. The core of our method is a new formal model of one type of perceptual structure, based on classical machine vision techniques for analyzing an image at multiple resolutions. The model takes as input an arbitrary grayscale image and returns a lattice structure describing the visual organization of the image. We show how this model captures several aspects of traditional design aesthetics, and we describe a software tool that implements the model to help designers analyze and refine visual displays. Our emphasis here is on demonstrating the model's potential as a design aid rather than as a description of human perception, but given its initial promise we propose a variety of ways in which the model could be extended and validated.

In visualising multidimensional data, it is well known that different types of algorithms to process them. Data sets might be distinguished according to volume, variable types and distribution, and each of these characteristics imposes constraints upon the choice of applicable algorithms for their visualization. Previous work has shown that a hybrid algorithmic approach can be successful in addressing the impact of data volume on the feasibility of multidimensional scaling (MDS). This suggests that hybrid combinations of appropriate algorithms might also successfully address other characteristics of data. This paper presents a system and framework in which a user can easily explore hybrid algorithms and the data flowing through them. Visual programming and a novel algorithmic architecture let the user semi-automatically define data flows and the co-ordination of multiple views.

This paper presents the results of an experiment aimed at investigating how different methods of viewing visual programs affect users' understanding. The first two methods used traditional flat and semantic zooming models of program representation; the third is a new representation that uses semantic zooming combined with blending and proximity. The results of several search tasks performed by approximately 80 participants showed that the new method resulted in both faster and more accurate searches than the other methods.

We present BARD (biological arc diagrams), a visualization tool for biological sequence analysis. The development of BARD began with the application of Wattenberg's arc diagrams [Wattenberg 2002] to results from sequence analysis programs, such as BLAST [Altschul et al. 1990]. In this paper, we extend the initial arc diagram concept in two ways: 1) by separating the visualization method from the underlying matching algorithm and 2) by expanding the types of matches to include inexact matches, complemented palindrome matches, and inter-sequence matches. BARD renders each type of match distinctly, resulting in a powerful tool to quickly understand sequence similarities and differences. We illustrate the power of BARD by applying the technique to a comparative sequence analysis of the human pathogenic fungi Cryptococcus neoformans.

Unlike traditional information visualization, ambient information visualizations reside in the environment of the user rather than on the screen of a desktop computer. Currently, most dynamic information that is displayed in public places consists of text and numbers. We argue that information visualization can be employed to make such dynamic data more useful and appealing. However, visualizations intended for non-desktop spaces will have to both provide valuable information and present an attractive addition to the environment - they must strike a balance between aesthetical appeal and usefulness. To explore this, we designed a real-time visualization of bus departure times and deployed it in a public space, with about 300 potential users. To make the presentation more visually appealing, we took inspiration from a modern abstract artist. The visualization was designed in two passes. First, we did a preliminary version that was presented to and discussed with prospective users. Based on their input, we did a final design. We discuss the lessons learned in designing this and previous ambient information visualizations, including how visual art can be used as a design constraint, and how the choice of information and the placement of the display affect the visualization.

We present Growing Polygons, a novel visualization technique for the graphical representation of causal relations and information flow in a system of interacting processes. Using this method, individual processes are displayed as partitioned polygons with color-coded segments showing dependencies to other processes. The entire visualization is also animated to communicate the dynamic execution of the system to the user. The results from a comparative user study of the method show that the Growing Polygons technique is significantly more efficient than the traditional Hasse diagram visualization for analysis tasks related to deducing information flow in a system for both small and large executions. Furthermore, our findings indicate that the correctness when solving causality tasks is significantly improved using our method. In addition, the subjective ratings of the users rank the method as superior in all regards, including usability, efficiency, and enjoyability.

This paper proposes a conceptual model called compound brushing for modeling the brushing techniques used in dynamic data visualization. In this approach, brushing techniques are modeled as higraphs with five types of basic entities: data, selection, device, renderer, and transformation. Using this model, a flexible visual programming tool is designed not only to configure/control various common types of brushing techniques currently used in dynamic data visualization, but also to investigate new brushing techniques.

The Informedia Digital Video Library user interface summarizes query results with a collage of representative keyframes. We present a user study in which keyframe occlusion caused difficulties. To use the screen space most efficiently to display images, both occlusion and wasted whitespace should be minimized. Thus optimal choices will tend toward constant density displays. However, previous constant density algorithms are based on global density, which leads to occlusion and empty space if the density is not uniform. We introduce an algorithm that considers the layout of individual objects and avoids occlusion altogether. Efficiency concerns are important for dynamic summaries of the Informedia Digital Video Library, which has hundreds of thousands of shots. Posting multiple queries that take into account parameters of the visualization as well as the original query reduces the amount of work required. This greedy algorithm is then compared to an optimal one. The approach is also applicable to visualizations containing complex graphical objects other than images, such as text, icons, or trees.

As visualization researchers, we are interested in gaining a better understanding of how to effectively use texture to facilitate shape perception. If we could design the ideal texture pattern to apply to an arbitrary smoothly curving shape to be most accurately and effectively perceived, what would the characteristics of that texture pattern be? In this paper we describe the results of a comprehensive controlled observer experiment intended to yield insight into that question. Here, we report the results of a new study comparing the relative accuracy of observers' judgments of shape type (elliptical, cylindrical, hyperbolic or flat) and shape orientation (convex, concave, both, or neither) for local views of boundary masked quadric surface patches under six different principal direction texture pattern conditions plus two texture conditions (an isotropic pattern and a non-principal direction oriented anisotropic pattern), under both perspective and orthographic projection conditions and from both head-on and oblique viewpoints. Our results confirm the hypothesis that accurate shape perception is facilitated to a statistically significantly greater extent by some principal direction texture patterns than by others. Specifically, we found that, for both views, under conditions of perspective projection, participants more often correctly identified the shape category and the shape orientation when the surface was textured with the pattern that contained oriented energy along both the first and second principal directions only than in the case of any other texture condition. Patterns containing markings following only one of the principal directions, or containing information along other directions in addition to the principal directions yielded poorer performance overall.

Microarrays are relatively new, high-throughput data acquisition technology for investigating biological phenomena at the micro-level. One of the more common procedures for microarray experimentation is that of the microarray time-course experiment. The product of microarray time-course experiment is time-series data, which subject to proper analysis has the potential to have significant impact on the diagnosis, treatment, and prevention of diseases. While existing information visualization techniques go some way to making microarray time-series data more manageable, requirements analysis has revealed significant limitations. The main finding was that users were unable to uncover and quantify common changes in value over a specified time-period. This paper describes a novel technique that provides this functionality by allowing the user to visually formulate and modify measurable queries with separate time-period and condition components. These visual queries are supported by the combination of a traditional value against time graph representation of the data with a complementary scatter-plot representation of a specified time-period. The multiple views of the visualization are coordinated so that the user can formulate and modify queries with rapid reversible display of query results in the traditional value against time graph format.

In this paper, we focus on some of the key design decisions we faced during the process of architecting a visualization system and present some possible choices, with their associated advantages and disadvantages. We frame this discussion within the context of Rivet, our general visualization environment designed for rapidly prototyping interactive, exploratory visualization tools for analysis. As we designed increasingly sophisticated visualizations, we needed to refine Rivet in order to be able to create these richer displays for larger and more complex data sets. The design decisions we discuss in this paper include: the internal data model, data access, semantic meta-data information the visualization can use to create effective visual decodings, the need for data transformations in a visualization tool, modular objects for flexibility, and the tradeoff between simplicity and expressiveness when providing methods for creating visualizations.

This paper reports on the development of a visualization system for architectural lighting designers. It starts by motivating the problem as both complex in its physics and social organization. Three iterations of prototypes for displaying time and space varying phenomena are discussed. Fieldwork is presented to identify where in practice they will be most effective. A set of user studies, one of which is analyzed in fine-grained detail, show how building designers incorporate visualization on hypothetical design problems. This has positive implications for both energy efficiency and lighting quality in buildings.

We introduce two dynamic visualization techniques using multidimensional scaling to analyze transient data streams such as newswires and remote sensing imagery. While the time-sensitive nature of these data streams requires immediate attention in many applications, the unpredictable and unbounded characteristics of this information can potentially overwhelm many scaling algorithms that require a full re-computation for every update. We present an adaptive visualization technique based on data stratification to ingest stream information adaptively when influx rate exceeds processing rate. We also describe an incremental visualization technique based on data fusion to project new information directly onto a visualization subspace spanned by the singular vectors of the previously processed neighboring data. The ultimate goal is to leverage the value of legacy and new information and minimize re-processing of the entire dataset in full resolution. We demonstrate these dynamic visualization results using a newswire corpus and a remote sensing imagery sequence.

An increasing number of tasks require people to explore, navigate and search extremely complex data sets visualized as graphs. Examples include electrical and telecommunication networks, Web structures, and airline routes. The problem is that graphs of these real world data sets have many interconnected nodes, ultimately leading to edge congestion: the density of edges is so great that they obscure nodes, individual edges, and even the visual information beneath the graph. To address this problem we developed an interactive technique called EdgeLens. An EdgeLens interactively curves graph edges away for a person's focus attention without changing the node positions. This opens up sufficient space to disambiguate node and edge relationships and to see underlying information while still preserving node layout. Initially two methods of creating this interaction were developed and compared in a user study. The results of this study were used in the selection of a basic approach and the subsequent development of the EdgeLens. We then improved the EdgeLens through use of transparency and colour and by allowing multiple lenses to appear on the graph.

Dynamic queries facilitate rapid exploration of information by real-time visual display of both query formulation and results. Dynamic query sliders are linked to the main visualization to filter data. A common alternative to dynamic queries is to link several simple visualizations, such as histograms, to the main visualization with a brushing interaction strategy. Selecting data in the histograms highlights that data in the main visualization. We compare these two approaches in an empirical experiment on DataMaps, a geographic data visualization tool. Dynamic query sliders resulted in better performance for simple range tasks, while brushing histograms was better for complex trend evaluation and attribute relation tasks. Participants preferred brushing histograms for understanding relationships between attributes and the rich information they provided.

High-resolution wall-size digital displays present significant new and different visual space to show and see imagery. The author has been working with two wall-size digital displays at Princeton University for five years and directing and producing IMAX films for a decade, and he has noted some unique design considerations for creating effective visual images when they are spread across entire walls. The author suggests these "frameless" screens - where images are so large we need to look around to see the entire field - need different ways of thinking about image design and visualization. Presenting such things as scale and detail take on new meaning when they can be displayed life-size and not shown in the context of one or many small frames such as we see everywhere. These design ideas will be of use for pervasive computing, interface research and design, interactive design, control design, representations of massive data sets, and creating effective displays of data for research and education.

We introduce an approach to visual analysis of multivariate data that integrates several methods from information visualization, exploratory data analysis (EDA), and geovisualization. The approach leverages the component-based architecture implemented in GeoVISTA Studio to construct a flexible, multiview, tightly (but generically) coordinated, EDA toolkit. This toolkit builds upon traditional ideas behind both small multiples and scatterplot matrices in three fundamental ways. First, we develop a general, multiform, bivariate matrix and a complementary multiform, bivariate small multiple plot in which different bivariate representation forms can be used in combination. We demonstrate the flexibility of this approach with matrices and small multiples that depict multivariate data through combinations of: scatterplots, bivariate maps, and space-filling displays. Second, we apply a measure of conditional entropy to (a) identify variables from a high-dimensional data set that are likely to display interesting relationships and (b) generate a default order of these variables in the matrix or small multiple display. Third, we add conditioning, a kind of dynamic query/filtering in which supplementary (undisplayed) variables are used to constrain the view onto variables that are displayed. Conditioning allows the effects of one or more well understood variables to be removed form the analysis, making relationships among remaining variables easier to explore. We illustrate the individual and combined functionality enabled by this approach through application to analysis of cancer diagnosis and mortality data and their associated covariates and risk factors.

An equity mutual fund is a financial instrument that invests in a set of stocks. Any two different funds may partially invest in some of the same stocks, thus overlap is common. Portfolio diversification aims at spreading an investment over many different stocks in search of greater returns. Helping people with portfolio diversification is challenging because it requires informing them about both their current portfolio of stocks held through funds and the other stocks in the market not invested in yet. Current stock/fund visualization systems either waste screen real estate and visualization of all data points. We have developed a system called FundExplorer that implements a distorted treemap to visualize both the amount of money invested in a person's fund portfolio and the context of remaining market stocks. The FundExplorer system enables people to interactively explore diversification possibilities with their portfolios.

The following topics are dealt with: computer displays; multiscaling; graphs; high dimensionality; occlusion; visualization evaluation; linking and design studies.