IEEE VIS Publication Dataset

Two tasks in graph visualization require partitioning: the assignment of visual attributes and divisive clustering. Often, we would like to assign a color or other visual attributes to a node or edge that indicates an associated value. In an application involving divisive clustering, we would like to partition the graph into subsets of graph elements based on metric values in such a way that all subsets are evenly populated. Assuming a uniform distribution of metric values during either partitioning or coloring can have undesired effects such as empty clusters or only one level of emphasis for the entire graph. Probability density functions derived from statistics about a metric can help systems succeed at these tasks

Radial, space-filling visualizations can be useful for depicting information hierarchies, but they suffer from one major problem. As the hierarchy grows in size, many items become small, peripheral slices that are difficult to distinguish. We have developed three visualization/interaction techniques that provide flexible browsing of the display. The techniques allow viewers to examine the small items in detail while providing context within the entire information hierarchy. Additionally, smooth transitions between views help users maintain orientation within the complete information space

By virtue of their spatio-cognitive abilities, humans are able to navigate through geographic space as well as meaningfully communicate geographic information represented in cartographic form. The current dominance of spatial metaphors in information visualization research is the result of the realization that those cognitive skills also have value in the exploration and analysis of non-geographic information. While mapping or landscape metaphors are routinely used in this field, there is a noticeable lack of consideration for existing cartographic expertise. This is especially apparent whenever problematic issues are encountered, such as graphic complexity or feature labeling. There are a number of areas in which a cartographic outlook could provide a valuable perspective. This paper discusses how geographic and cartographic notions may influence the design of visualizations for textual information spaces. Map projections, generalization, feature labeling and map design issues are discussed

Since novice users of visualization systems lack knowledge and expertise in data visualization, it is a tough task for them to generate efficient and effective visualizations that allow them to comprehend information that is embedded in the data. Therefore, systems supporting the users to design appropriate visualizations are of great importance. The GADGET (Goal-oriented Application Design Guidance for modular visualization EnvironmenTs) system, which has been developed by the authors (1997), interactively helps users to design scientific visualization applications by presenting appropriate MVE (Modular Visualization Environment) prototypes according to the specification of the visualization goals expressed mainly with the Wehrend matrix (S. Wehrend & C. Lewis, 1990). This paper extends this approach in order to develop a system named GADGET/IV, which is intended to provide the users with an environment for semi-automatic design of information visualization (IV) applications. To this end, a novel goal-oriented taxonomy of IV techniques is presented. Also, an initial design of the system architecture and user assistance flow is described. The usefulness of the GADGET/IV system is illustrated with example problems of Web site access frequency analysis

Data visualization environments help users understand and analyze their data by permitting interactive browsing of graphical representations of the data. To further facilitate understanding and analysis, many visualization environments have special features known as portals, which are sub-windows of a data canvas. Portals provide a way to display multiple graphical representations simultaneously, in a nested fashion. This makes portals an extremely powerful and flexible paradigm for data visualization. Unfortunately, with this flexibility comes complexity. There are over a hundred possible ways each portal can be configured to exhibit different behaviors. Many of these behaviors are confusing and certain behaviors can be inappropriate for a particular setting. It is desirable to eliminate confusing and inappropriate behaviors. The authors construct a taxonomy of portal behaviors and give recommendations to help designers of visualization systems decide which behaviors are intuitive and appropriate for a particular setting. They apply these recommendations to an example setting that is fully visually programmable and analyze the resulting reduced set of behaviors. Finally, the authors consider a real visualization environment and demonstrate some problems associated with behaviors that do not follow their recommendations

With an increase in the number of different visualization techniques, it becomes necessary to develop a measure for evaluating the effectiveness of visualizations. Metrics to evaluate visual displays were developed based on measures of information content developed by Shannon and used in communication theory. These measures of information content can be used to quantify the relative effectiveness of displays

COMIND is a tool for conceptual design of industrial products. It helps designers define and evaluate the initial design space by using search algorithms to generate sets of feasible solutions. Two algorithm visualization techniques, Kaleidoscope and Lattice, and one visualization of n-dimensional data, MAP, are used to externalize the machine's problem solving strategies and the tradeoffs as a result of using these strategies. After a short training period, users are able to discover tactics to explore design space effectively, evaluate new design solutions, and learn important relationships among design criteria, search speed and solution quality. We thus propose that visualization can serve as a tool for interactive intelligence, ie., human-machine collaboration for solving complex problems

Lighthouse is an on-line interface for a Web-based information retrieval system. It accepts queries from a user, collects the retrieved documents from the search engine, organizes and presents them to the user. The system integrates two known presentations of the retrieved results, the ranked list and clustering visualization, in a novel and effective way. It accepts the user's input and adjusts the document visualization accordingly. We give a brief overview of the system

Anyone who has ever experienced three-dimensional (3D) interfaces will agree that navigating in a 3D world is not a trivial task. The user interface of traditional 3D browsers provides simple navigation tools that allow the user to modify the camera parameters such as orientation, position and focal. Using these tools, it is frequent that, after some movements, the user is lost in the virtual 3D space and usually tries to restart from the beginning. We present how the 3D navigation problem is addressed in the context of the CyberNet project (Abel et al., 2000). Our underlying principle is to help the user navigate by adapting the navigation tool to the virtual world. We feel that the navigation schemes provided by the 3D browsers are too generic for some specific 3D tools and we have developed adaptive navigation features that are dependent on the 3D metaphor used for visualizing the information and on the user's task

One area in need of new research in information visualization is the operation and analysis of large-scale electric power systems. In analyzing power systems, one is usually confronted with a large amount of multivariate data. With systems containing tens of thousands of electrical nodes (buses), a key challenge is to present this data in a form so the user can assess the state of the system in an intuitive and quick manner. This is particularly true when trying to analyze relationships between actual network power flows, the scheduled power flows, and the capacity of the transmission system. With electric industry restructuring and the move towards having a single entity, such as an independent system operator or pool, operate a much larger system, this need has become more acute. This paper presents several power system visualization techniques to help in this task. These techniques include animation of power system flow values, contouring of bus and transmission line flow values, data aggregation techniques and interactive 3D data visualization

In the last several years, large multi-dimensional databases have become common in a variety of applications such as data warehousing and scientific computing. Analysis and exploration tasks place significant demands on the interfaces to these databases. Because of the size of the data sets, dense graphical representations are more effective for exploration than spreadsheets and charts. Furthermore, because of the exploratory nature of the analysis, it must be possible for the analysts to change visualizations rapidly as they pursue a cycle involving first hypothesis and then experimentation. The authors present Polaris, an interface for exploring large multi-dimensional databases that extends the well-known Pivot Table interface. The novel features of Polaris include an interface for constructing visual specifications of table based graphical displays and the ability to generate a precise set of relational queries from the visual specifications. The visual specifications can be rapidly and incrementally developed, giving the analyst visual feedback as they construct complex queries and visualizations

The increasing diversity of computers, especially among small mobile devices such as mobile phones and PDAs, raise new questions about information visualization techniques developed for the desktop computer. Using a series of examples ranging from applications for ordinary desktop displays to web-browsers and other applications for PDAs, we describe how a focus+context technique, Flip Zooming, is changed due to the situation it is used in. Based on these examples, we discuss how the use of ÔÇ£focusÔÇØ and ÔÇ£contextÔÇØ in focus+context techniques change in order to fit new areas of use for information visualization

ThemeRiverTM is a prototype system that visualizes thematic variations over time within a large collection of documents. The “river” flows from left to right through time, changing width to depict changes in thematic strength of temporally associated documents. Colored “currents” flowing within the river narrow or widen to indicate decreases or increases in the strength of an individual topic or a group of topics in the associated documents. The river is shown within the context of a timeline and a corresponding textual presentation of external events

This paper introduces a number of general methods for visualizing commonality in sets of text files. Each visualization simultaneously compares one file in the set to all other files in the set. These visualizations, which can be computed in O(n) time and space, are explained and then applied to the problem of detecting plagiarism in large computer science classes. A case study is presented and sample visualizations are provided. Finally, a new interactive tool that can be used to produce and manipulate these visualizations is presented.

We describe MGV, an integrated visualization and exploration system for massive multi-digraph navigation. MGV's only assumption is that the vertex set of the underlying digraph corresponds to the set of leaves of a predetermined tree T. MGV builds an out-of-core graph hierarchy and provides mechanisms to plug in arbitrary visual representations for each graph hierarchy slice. Navigation from one level to another of the hierarchy corresponds to the implementation of a drill-down interface. In order to provide the user with navigation control and interactive response, MGV incorporates a number of visualization techniques like interactive pixel-oriented 2D and 3D maps, statistical displays, multi-linked views, and a zoomable label based interface. This makes the association of geographic information and graph data very natural. MGV follows the client-server paradigm and it is implemented in C and Java-3D. We highlight the main algorithmic and visualization techniques behind the tools and point out along the way several possible application scenarios. Our techniques are being applied to multi-graphs defined on vertex sets with sizes ranging from 100 million to 250 million vertices

A sequential pattern in data mining is a finite series of elements such as ABCD where A, B, C, and D are elements of the same domain. The mining of sequential patterns is designed to find patterns of discrete events that frequently happen in the same arrangement along a timeline. Like association and clustering, the mining of sequential patterns is among the most popular knowledge discovery techniques that apply statistical measures to extract useful information from large datasets. As out computers become more powerful, we are able to mine bigger datasets and obtain hundreds of thousands of sequential patterns in full detail. With this vast amount of data, we argue that neither data mining nor visualization by itself can manage the information and reflect the knowledge effectively. Subsequently, we apply visualization to augment data mining in a study of sequential patterns in large text corpora. The result shows that we can learn more and more quickly in an integrated visual data-mining environment

We propose a novel approach for segmentation and digital cleansing of endoscopic organs. Our method can be used for a variety of segmentation needs with little or no modification. It aims at fulfilling the dual and often conflicting requirements of a fast and accurate segmentation and also eliminates the undesirable partial volume effect which contemporary approaches cannot. For segmentation and digital cleansing, we use the peculiar characteristics exhibited by the intersection of any two distinct-intensity regions. To detect these intersections we cast rays through the volume, which we call the segmentation rays as they assist in the segmentation. We then associate a certain task of reconstruction and classification with each intersection the ray detects. We further use volumetric contrast enhancement to reconstruct surface lost by segmentation (if any), which aids in improving the quality of the volume rendering.

The paper describes a computer modeling and simulation system that supports computational steering, which is an effort to make the typical simulation workflow more efficient. Our system provides an interface that allows scientists to perform all of the steps in the simulation process in parallel and online. It uses a standard network flow visualization package, which has been extended to display graphical output in an immersive virtual environment such as a CAVE. Our system allows scientists to interactively manipulate simulation parameters and observe the results. It also supports inverse steering, where the user specifies the desired simulation result, and the system searches for the simulation parameters that achieve this result. Taken together, these capabilities allow scientists to more efficiently and effectively understand model behavior, as well as to search through simulation parameter space. The paper is also a case study of applying our system to the problem of simulating microwave interactions with missile bodies. Because these interactions are difficult to study experimentally, and have important effects on missile electronics, there is a strong desire to develop and validate simulation models of this phenomena.

A new method for the simplification of flow fields is presented. It is based on continuous clustering. A well-known physical clustering model, the Cahn Hillard model which describes phase separation, is modified to reflect the properties of the data to be visualized. Clusters are defined implicitly as connected components of the positivity set of a density function. An evolution equation for this function is obtained as a suitable gradient flow of an underlying anisotropic energy functional. Here, time serves as the scale parameter. The evolution is characterized by a successive coarsening of patterns—the actual clustering — and meanwhile the underlying simulation data specifies preferable pattern boundaries. Here we discuss the applicability of this new type of approach mainly for flow fields, where the cluster energy penalizes cross streamline boundaries, but the method also carries provisions in other fields as well. The clusters are visualized via iconic representations. A skeletonization algorithm is used to find suitable positions for the icons.

The paper presents a seed placement strategy for streamlines based on flow features in the dataset. The primary goal of our seeding strategy is to capture flow patterns in the vicinity of critical points in the flow field, even as the density of streamlines is reduced. Secondary goals are to place streamlines such that there is sufficient coverage in non-critical regions, and to vary the streamline placements and lengths so that the overall presentation is aesthetically pleasing (avoid clustering of streamlines, avoid sharp discontinuities across several streamlines, etc.). The procedure is straightforward and non-iterative. First, critical points are identified. Next, the flow field is segmented into regions, each containing a single critical point. The critical point in each region is then seeded with a template depending on the type of critical point. Finally, additional seed points are randomly distributed around the field using a Poisson disk distribution to minimize closely spaced seed points. The main advantage of this approach is that it does not miss the features around critical points. Since the strategy is not image-guided, and hence not view dependent, significant savings are possible when examining flow fields from different viewpoints, especially for 3D flow fields.