IEEE VIS Publication Dataset

We present a novel approach to out-of-core time-varying isosurface visualization. We attempt to interactively visualize time-varying datasets which are too large to fit into main memory using a technique which is dramatically different from existing algorithms. Inspired by video encoding techniques, we examine the data differences between time steps to extract isosurface information. We exploit span space extraction techniques to retrieve operations necessary to update isosurface geometry from neighboring time steps. Because only the changes between time steps need to be retrieved from disk, I/O bandwidth requirements are minimized. We apply temporal compression to further reduce disk access and employ a point-based previewing technique that is refined in idle interaction cycles. Our experiments on computational simulation data indicate that this method is an extremely viable solution to large time-varying isosurface visualization. Our work advances the state-of-the-art by enabling all isosurfaces to be represented by a compact set of operations

This paper describes a set of visual cues of contact designed to improve the interactive manipulation of virtual objects in industrial assembly/maintenance simulations. These visual cues display information of proximity, contact and effort between virtual objects when the user manipulates a part inside a digital mock-up. The set of visual cues encloses the apparition of glyphs (arrow, disk, or sphere) when the manipulated object is close or in contact with another part of the virtual environment. Light sources can also be added at the level of contact points. A filtering technique is proposed to decrease the number of glyphs displayed at the same time. Various effects - such as change in color, change in size, and deformation of shape - can be applied to the glyphs as a function of proximity with other objects or amplitude of the contact forces. A preliminary evaluation was conducted to gather the subjective preference of a group of participants during the simulation of an automotive assembly operation. The collected questionnaires showed that participants globally appreciated our visual cues of contact. The changes in color appeared to be preferred concerning the display of distances and proximity information. Size changes and deformation effects appeared to be preferred in terms of perception of contact forces between the parts. Last, light sources were selected to focus the attention of the user on the contact areas

Video visualization is a computation process that extracts meaningful information from original video data sets and conveys the extracted information to users in appropriate visual representations. This paper presents a broad treatment of the subject, following a typical research pipeline involving concept formulation, system development, a path-finding user study, and a field trial with real application data. In particular, we have conducted a fundamental study on the visualization of motion events in videos. We have, for the first time, deployed flow visualization techniques in video visualization. We have compared the effectiveness of different abstract visual representations of videos. We have conducted a user study to examine whether users are able to learn to recognize visual signatures of motions, and to assist in the evaluation of different visualization techniques. We have applied our understanding and the developed techniques to a set of application video clips. Our study has demonstrated that video visualization is both technically feasible and cost-effective. It has provided the first set of evidence confirming that ordinary users can be accustomed to the visual features depicted in video visualizations, and can learn to recognize visual signatures of a variety of motion events

In this paper we propose an approach in which interactive visualization and analysis are combined with batch tools for the processing of large data collections. Large and heterogeneous data collections are difficult to analyze and pose specific problems to interactive visualization. Application of the traditional interactive processing and visualization approaches as well as batch processing encounter considerable drawbacks for such large and heterogeneous data collections due to the amount and type of data. Computing resources are not sufficient for interactive exploration of the data and automated analysis has the disadvantage that the user has only limited control and feedback on the analysis process. In our approach, an analysis procedure with features and attributes of interest for the analysis is defined interactively. This procedure is used for offline processing of large collections of data sets. The results of the batch process along with "visual summaries" are used for further analysis. Visualization is not only used for the presentation of the result, but also as a tool to monitor the validity and quality of the operations performed during the batch process. Operations such as feature extraction and attribute calculation of the collected data sets are validated by visual inspection. This approach is illustrated by an extensive case study, in which a collection of confocal microscopy data sets is analyzed

Thread-like structures are becoming more common in modern volumetric data sets as our ability to image vascular and neural tissue at higher resolutions improves. The thread-like structures of neurons and micro-vessels pose a unique problem in visualization since they tend to be densely packed in small volumes of tissue. This makes it difficult for an observer to interpret useful patterns from the data or trace individual fibers. In this paper we describe several methods for dealing with large amounts of thread-like data, such as data sets collected using knife-edge scanning microscopy (KESM) and serial block-face scanning electron microscopy (SBF-SEM). These methods allow us to collect volumetric data from embedded samples of whole-brain tissue. The neuronal and microvascular data that we acquire consists of thin, branching structures extending over very large regions. Traditional visualization schemes are not sufficient to make sense of the large, dense, complex structures encountered. In this paper, we address three methods to allow a user to explore a fiber network effectively. We describe interactive techniques for rendering large sets of neurons using self-orienting surfaces implemented on the GPU. We also present techniques for rendering fiber networks in a way that provides useful information about flow and orientation. Third, a global illumination framework is used to create high-quality visualizations that emphasize the underlying fiber structure. Implementation details, performance, and advantages and disadvantages of each approach are discussed

Vortices are undesirable in many applications while indispensable in others. It is therefore of common interest to understand their mechanisms of creation. This paper aims at analyzing the transport of vorticity inside incompressible flow. The analysis is based on the vorticity equation and is performed along pathlines which are typically started in upstream direction from vortex regions. Different methods for the quantitative and explorative analysis of vorticity transport are presented and applied to CFD simulations of water turbines. Simulation quality is accounted for by including the errors of meshing and convergence into analysis and visualization. The obtained results are discussed and interpretations with respect to engineering questions are given

In order to understand complex vortical flows in large data sets, we must be able to detect and visualize vortices in an automated fashion. In this paper, we present a feature-based vortex detection and visualization technique that is appropriate for large computational fluid dynamics data sets computed on unstructured meshes. In particular, we focus on the application of this technique to visualization of the flow over a serrated wing and the flow field around a spinning missile with dithering canards. We have developed a core line extraction technique based on the observation that vortex cores coincide with local extrema in certain scalar fields. We also have developed a novel technique to handle complex vortex topology that is based on k-means clustering. These techniques facilitate visualization of vortices in simulation data that may not be optimally resolved or sampled. Results are included that highlight the strengths and weaknesses of our approach. We conclude by describing how our approach can be improved to enhance robustness and expand its range of applicability

A recent line of treemap research has focused on layout algorithms that optimize properties such as stability, preservation of ordering information, and aspect ratio of rectangles. No ideal treemap layout algorithm has been found, and so it is natural to explore layouts that produce nonrectangular regions. This note describes a connection between space-filling visualizations and the mathematics of space-filling curves, and uses that connection to characterize a family of layout algorithms which produce nonrectangular regions but enjoy geometric continuity under changes to the data and legibility even for highly unbalanced trees.

It has long been known that ancient temples were frequently oriented along the cardinal directions or to certain points along the horizon where Sun or Moon rise or set on special days of the year. In the last decades, archaeologists have found evidence of even older building structures buried in the soil, with doorways that also appear to have distinct orientations. This paper presents a novel diagram combining archaeological maps with a folded-apart, flattened view of the whole sky, showing the local horizon and the daily paths of Sun, Moon and brighter stars. By use of this diagram, interesting groupings of astronomical orientation directions, e.g. to certain Sunrise and Sunset points could be identified, which were evidently used to mark certain days of the year. Orientations to a few significant stars very likely indicated the beginning of the agricultural year in the middle neolithic period

The usability of knowledge domain visualization (KDViz) tools can be assessed at several levels. Cognitive walkthrough (CW) is a well known usability inspection method that focuses on how easily users can learn software through exploration. Typical applications of CW follow structured tasks where user goals and action sequences that lead to achievement of the goals are well defined. KDViz and other information visualization tools, however, are typically designed for users to explore data and user goals and actions are less well understood. In this paper, we describe how the traditional CW method may be adapted for assessing the usability of these systems. We apply the adapted version of CW to CiteSpace, a KDViz tool that uses bibliometric analyses to create visualizations of scientific literatures. We describe usability issues identified by the adapted CW and discuss how CiteSpace supported the completion of tasks, such as identifying research fronts, and the achievement of goals. Finally, we discuss improvements to the adapted CW and issues to be addressed before applying it to a wider range of KDViz tools.

Space-filling visualizations, such as the TreeMap, are well suited for displaying the properties of nodes in hierarchies. To browse the contents of the hierarchy, the primary mode of interaction is by drilling down through many successive layers. In this paper we introduce a distortion algorithm based on fisheye and continuous zooming techniques for browsing data in the TreeMap representation. The motivation behind the distortion approach is for assisting users to rapidly browse information displayed in the TreeMap without opening successive layers of the hierarchy. Two experiments were conducted to evaluate the new approach. In the first experiment (N=20) the distortion approach is compared to the drill down method. Results show that subjects are quicker and more accurate in locating targets of interest using the distortion method. The second experiment (N=12) evaluates the effectiveness of the two approaches in a task requiring context, we define as the context browsing task. The results show that subjects are quicker and more accurate in locating targets with the distortion technique in the context browsing task.

Parallel coordinates are a powerful method for visualizing multidimensional data but, when applied to large data sets, they become cluttered and difficult to read. Star glyphs, on the other hand, can be used to display either the attributes of a data item or the values across all items for a single attribute. Star glyphs may readily provide a quick impression; however, since the full data set require multiple glyphs, overall readings are more difficult. We present parallel glyphs, an interactive integration of the visual representations of parallel coordinates and star glyphs that utilizes the advantages of both representations to offset the disadvantages they have separately. We discuss the role of uniform and stepped colour scales in the visual comparison of non-adjacent items and star glyphs. Parallel glyphs provide capabilities for focus-in-context exploration using two types of lenses and interactions specific to the 3D space.

We are building an intelligent multimodal conversation system to aid users in exploring large and complex data sets. To tailor to diverse user queries introduced during a conversation, we automate the generation of system responses, including both spoken and visual outputs. In this paper, we focus on the problem of visual context management, a process that dynamically updates an existing visual display to effectively incorporate new information requested by subsequent user queries. Specifically, we develop an optimization based approach to visual context management. Compared to existing approaches, which normally handle predictable visual context updates, our work offers two unique contributions. First, we provide a general computational framework that can effectively manage a visual context for diverse, unanticipated situations encountered in a user system conversation. Moreover, we optimize the satisfaction of both semantic and visual constraints, which otherwise are difficult to balance using simple heuristics. Second, we present an extensible representation model that uses feature based metrics to uniformly define all constraints. We have applied our work to two different applications and our evaluation has shown the promise of this work.

The Name Voyager, a Web based visualization of historical trends in baby naming, has proven remarkably popular. This paper discusses the interaction techniques it uses for smooth visual exploration of thousands of time series. We also describe design decisions behind the application and lessons learned in creating an application that makes do-it-yourself data mining popular. The prime lesson, it is hypothesized, is that an information visualization tool may be fruitfully viewed not as a tool but as part of an online social environment. In other words, to design a successful exploratory data analysis tool, one good strategy is to create a system that enables "social" data analysis

We describe a new method for visualization of directed graphs. The method combines constraint programming techniques with a high performance force directed placement (FDP) algorithm so that the directed nature of the graph is highlighted while useful properties of FDP - such as emphasis of symmetries and preservation of proximity relations - are retained. Our algorithm automatically identifies those parts of the digraph that contain hierarchical information and draws them accordingly. Additionally, those parts that do not contain hierarchy are drawn at the same quality expected from a nonhierarchical, undirected layout algorithm. An interesting application of our algorithm is directional multidimensional scaling (DMDS). DMDS deals with low dimensional embedding of multivariate data where we want to emphasize the overall flow in the data (e.g. chronological progress) along one of the axes.

The paper describes a novel technique to visualize graphs with extended node and link labels. The lengths of these labels range from a short phrase to a full sentence to an entire paragraph and beyond. Our solution is different from all the existing approaches that almost always rely on intensive computational effort to optimize the label placement problem. Instead, we share the visualization resources with the graph and present the label information in static, interactive, and dynamic modes without the requirement for tackling the intractability issues. This allows us to reallocate the computational resources for dynamic presentation of real time information. The paper includes a user study to evaluate the effectiveness and efficiency of the visualization technique.

We investigate the use of elastic hierarchies for representing trees, where a single graphical depiction uses a hybrid mixture, or "interleaving", of more basic forms at different nodes of the tree. In particular, we explore combinations of node link and treemap forms, to combine the space efficiency of treemaps with the structural clarity of node link diagrams. A taxonomy is developed to characterize the design space of such hybrid combinations. A software prototype is described, which we used to explore various techniques for visualizing, browsing and interacting with elastic hierarchies, such as side by side overview and detail views, highlighting and rubber banding across views, visualization of multiple foci, and smooth animations across transitions. The paper concludes with a discussion of the characteristics of elastic hierarchies and suggestions for research on their properties and uses.

Cartographers have long used flow maps to show the movement of objects from one location to another, such as the number of people in a migration, the amount of goods being traded, or the number of packets in a network. The advantage of flow maps is that they reduce visual clutter by merging edges. Most flow maps are drawn by hand and there are few computer algorithms available. We present a method for generating flow maps using hierarchical clustering given a set of nodes, positions, and flow data between the nodes. Our techniques are inspired by graph layout algorithms that minimize edge crossings and distort node positions while maintaining their relative position to one another. We demonstrate our technique by producing flow maps for network traffic, census data, and trade data.

We introduce Tukey and Tukey scagnostics and develop graph-theoretic methods for implementing their procedure on large datasets.

We present a method for visual summary of bilateral conflict structures embodied in event data. Such data consists of actors linked by time stamped events, and may be extracted from various sources such as news reports and dossiers. When analyzing political events, it is of particular importance to be able to recognize conflicts and actors involved in them. By projecting actors into a conflict space, we are able to highlight the main opponents in a series of tens of thousands of events, and provide a graphic overview of the conflict structure. Moreover, our method allows for smooth animation of the dynamics of a conflict.