IEEE VIS Publication Dataset

Beamtrees are a new method for the visualization of large hierarchical data sets. Nodes are shown as stacked circular beams, such that both the hierarchical structure as well as the size of nodes are depicted. The dimensions of beams are calculated using a variation of the treemap algorithm. A small user study indicated that beamtrees are significantly more effective than nested treemaps and cushion treemaps for the extraction of global hierarchical information.

Example-based graphics generation systems automatically create new information visualizations by learning from existing graphic examples. As part of the effort on developing a general-purpose example-based generation system, we are building a visual database of graphic examples. In this paper, we address two main issues involved in constructing such a database: example selection and example modeling. As a result, our work offers three unique contributions: First, we build a visual database that contains a diverse collection of well-designed examples. Second, we develop a feature-based scheme to model all examples uniformly and accurately. Third, our visual database brings several important implications to the area of information visualization.

We describe a visualization tool which allows a biologist to explore a large set of hypothetical evolutionary trees. Interacting with such a dataset allows the biologist to identify distinct hypotheses about how different species or organisms evolved, which would not have been clear from traditional analyses. Our system integrates a point-set visualization of the distribution of hypothetical trees with detail views of an individual tree, or of a consensus tree summarizing a subset of trees. Efficient algorithms were required for the key tasks of computing distances between trees, finding consensus trees, and laying out the point-set visualization.

Since the crash of the dot.coms, investors have gotten a lot more careful with where they place their money. Now more than ever it becomes really important for venture capitalists (VCs) to monitor the state of the startups market and continually update their investment strategy to suit the rapidly changing market conditions. This paper presents three new visualization metaphors (Spiral Map, TimeTicker, and Double Histogram) for monitoring the startups market. While we are focusing on the VC domain, the visual metaphors developed are general and can be easily applied to other domains.

Cartograms are a well-known technique for showing geography-related statistical information, such as population demographics and epidemiological data. The basic idea is to distort a map by resizing its regions according to a statistical parameter, but in a way that keeps the map recognizable. We deal with the problem of making continuous cartograms that strictly retain the topology of the input mesh. We compare two algorithms to solve the continuous cartogram problem. The first one uses an iterative relocation of the vertices based on scanlines. The second one is based on the Gridfit technique, which uses pixel-based distortion based on a quadtree-like data structure.

Research in several areas provides scientific guidance for use of graphical encoding to convey information in an information visualization display. By graphical encoding we mean the use of visual display elements such as icon color, shape, size, or position to convey information about objects represented by the icons. Literature offers inconclusive and often conflicting viewpoints, including the suggestion that the effectiveness of a graphical encoding depends on the type of data represented. Our empirical study suggests that the nature of the users' perceptual task is more indicative of the effectiveness of a graphical encoding than the type of data represented.

Existing information visualization techniques are usually limited to the display of a few thousand items. This article describes new interactive techniques capable of handling a million items (effectively visible and manageable on screen). We evaluate the use of hardware-based techniques available with newer graphics cards, as well as new animation techniques and non-standard graphical features such as stereovision and overlap count. These techniques have been applied to two popular information visualizations: treemaps and scatter plot diagrams; but are generic enough to be applied to other 2D representations as well.

For a decade, the ruling common wisdom for Internet traffic held that it was everywhere bursty: over periods lasting tens of milliseconds to hundreds, the traffic was either much below its average rate or much above. In other words, the traffic was not smooth, not staying at all times close to its average. It was bursty on the cable running down a street, carrying the merged traffic of a small number of cable modem users in one section of a town. It was bursty on the core fiber of an Internet service provider, carrying the merged traffic of thousands of users from all over the country. The Internet was designed to accommodate the bursty traffic. The routers and switches that forward traffic from one place to the next were designed for burstiness, and Internet service providers allocated traffic loads on the devices based on an assumption of burstiness. Recently, it was discovered that the old common wisdom is not true. Visualization played a fundamental role in the discovery. The old wisdom held up for links with a small numbers of users. But as the number of users increases, the burstiness dissipates, and the traffic becomes smooth. Design of the high-load part of the Internet needs to be rethought. The old wisdom had persisted for high-load links because the databases of traffic measurements from them are immense, and the traffic measurements had not been studied in their fullest detail, which is necessary to see the smoothing. Visualization tools allowed the detail to be seen, and allowed the verification of a mathematical theory that predicts the smoothing. To see the detail, individual visual displays were created that take up an amount of virtual screen real estate measured in hundreds of pages. It is a simple idea: if you have a lot of data, and you want to see it in detail, you need a lot of space. What is needed now is a rich set of ideas and methods for navigating such very large displays.

Radial, space-filling (RSF) techniques for hierarchy visualization have several advantages over traditional node-link diagrams, including the ability to efficiently use the display space while effectively conveying the hierarchy structure. Several RSF systems and tools have been developed to date, each with varying degrees of support for interactive operations such as selection and navigation. We describe what we believe to be a complete set of desirable operations on hierarchical structures. We then present InterRing, an RSF hierarchy visualization system that supports a significantly more extensive set of these operations than prior systems. In particular, InterRing supports multi-focus distortions, interactive hierarchy reconfiguration, and both semi-automated and manual selection. We show the power and utility of these and other operations, and describe our on-going efforts to evaluate their effectiveness and usability.

Information analysis often involves decomposing data into sub-groups to allow for comparison and identification of relationships. Breakdown Visualization provides a mechanism to support this analysis through user guided drill-down of polyarchical metadata. This metadata describes multiple hierarchical structures for organizing tuple aggregations and table attributes. This structure is seen in financial data, organizational structures, sport statistics, and other domains. A spreadsheet format enables comparison of visualizations at any level of the hierarchy. Breakdown Visualization allows users to drill-down a single hierarchy then pivot into another hierarchy within the same view. It utilizes a fix and move technique that allows users to select multiple foci for drill-down.

Most analysts start with an overview of the data before gradually refining their view to be more focused and detailed. Multiscale pan-and-zoom systems are effective because they directly support this approach. However generating abstract overviews of large data sets is difficult, and most systems take advantage of only one type of abstraction: visual abstraction. Furthermore, these existing systems limit the analyst to a single zooming path on their data and thus a single set of abstract views. This paper presents: (1) a formalism for describing multiscale visualizations of data cubes with both data and visual abstraction, and (2) a method for independently zooming along one or more dimensions by traversing a zoom graph with nodes at different levels of detail. As an example of how to design multiscale visualizations using our system, we describe four design patterns using our formalism. These design patterns show the effectiveness of multiscale visualization of general relational databases.

We demonstrate how we apply information visualization techniques to process monitoring. Virtual instruments are enhanced using history encoding instruments are capable of displaying the current value and the value from the near past. Multi-instruments are capable of displaying several data sources simultaneously. Levels of detail for virtual instruments are introduced where the screen area is inversely proportional to the information amount displayed. Furthermore the monitoring system is enhanced by using: 3D anchoring attachment of instruments to positions on a 3D model, collision avoidance a physically based spring model prevents instruments from overlapping, and focus+context rendering - giving the user a possibility to examine particular instruments in detail without loosing the context information.

We present a novel tree browser that builds on the conventional node link tree diagrams. It adds dynamic rescaling of branches of the tree to best fit the available screen space, optimized camera movement, and the use of preview icons summarizing the topology of the branches that cannot be expanded. In addition, it includes integrated search and filter functions. This paper reflects on the evolution of the design and highlights the principles that emerged from it. A controlled experiment showed benefits for navigation to already previously visited nodes and estimation of overall tree topology.

A large body of results on the characteristics of human spatial vision suggests that space perception is distorted. Recent studies indicate that the geometry of visual space is best understood as Affine. If this is the case, it has far reaching implications on how 3D visualizations can be successfully employed. For instance, all attempts to build visualization systems where users are expected to discover relations based on Euclidean distances or shapes will be ineffective. Because visualization can, and sometimes do, employ all possible types of depth information and because the results from vision research usually concentrates on one or two such types, three experiments were performed under near optimal viewing conditions. The aim of the experiments was twofold: To test whether the earlier findings generalize to optimal viewing conditions and to get a sense of the size of the error under such conditions. The results show that the findings do generalize and that the errors are large. The implications of these results for successful visualizations are discussed.

We describe a system for analyzing the flow of traffic through Web sites. We decomposed the general path analysis problem into a set of distinct subproblems, and created a visual metaphor for analyzing each of them. Our system works off of multiple representations of the clickstream, and exposes the path extraction algorithms and data to the visual metaphors as Web services. We have combined the visual metaphors into a Web-based "path analysis portal" that lets the user easily switch between the different modes of analysis.

A new method for visualizing the class of incrementally evolving networks is presented. In addition to the intermediate states of the network it conveys the nature of the change between them by unrolling the dynamics of the network. Each modification is shown in a separate layer of a three-dimensional representation, where the stack of layers corresponds to a time line of the evolution. We focus on discourse networks as the driving application, but our method extends to any type of network evolving in similar ways.

Relational databases provide significant flexibility to organize, store, and manipulate an infinite variety of complex data collections. This flexibility is enabled by the concept of relational data schemas, which allow data owners to easily design custom databases according to their unique needs. However, user interfaces and information visualizations for accessing and utilizing databases have not kept pace with this level of flexibility. This paper introduces the concept of visualization schemas, based on the Snap-Together Visualization model, which are analogous to relational data schemas. Visualization schemas enable users to rapidly construct customized multiple-view visualizations for databases in a similarly flexible fashion without programming. Since the design of appropriate visualizations for a given database depends on the data schema, visualization schemas are a natural analogy to the data schema concept.

Data-mining of information by the process of pattern discovery in protein sequences has been predominantly algorithm based. We discuss a visualization approach, which uses texture mapping and blending techniques to perform visual data-mining on text data obtained from discovering patterns in protein sequences. This visual approach, investigates the possibilities of representing text data in three dimensions and provides new possibilities of representing more dimensions of information in text data visualization and analysis. We also present a generic framework derived from this visualization approach to visualize text in biosequence data.

Visualization is a powerful way to facilitate data analysis, but it is crucial that visualization systems explicitly convey the presence, nature, and degree of uncertainty to users. Otherwise, there is a danger that data will be falsely interpreted, potentially leading to inaccurate conclusions. A common method for denoting uncertainty is to use error bars or similar techniques designed to convey the degree of statistical uncertainty. While uncertainty can often be modeled statistically, a second form of uncertainty, bounded uncertainty, can also arise that has very different properties than statistical uncertainty. Error bars should not be used for bounded uncertainty because they do not convey the correct properties, so a different technique should be used instead. We describe a technique for conveying bounded uncertainty in visualizations and show how it can be applied systematically to common displays of abstract charts and graphs. Interestingly, it is not always possible to show the exact degree of uncertainty, and in some cases it can only be displayed approximately.