J.J. van Wijk

Cluster and calendar based visualization of time series data

A new method is presented to get an insight into univariate time series data. The problem addressed is how to identify patterns and trends on multiple time scales (days, weeks, seasons) simultaneously. The solution presented is to cluster similar daily data patterns, and to visualize the average patterns as graphs and the corresponding days on a calendar. This presentation provides a quick insight into both standard and exceptional patterns. Furthermore, it is well suited to interactive exploration. Two applications, numbers of employees present and energy consumption, are presented.A new method is presented to get an insight into univariate time series data. The problem addressed is how to identify patterns and trends on multiple time scales (days, weeks, seasons) simultaneously. The solution presented is to cluster similar daily data patterns, and to visualize the average patterns as graphs and the corresponding days on a calendar. This presentation provides a quick insight into both standard and exceptional patterns. Furthermore, it is well suited to interactive exploration. Two applications, numbers of employees present and energy consumption, are presented.

Visual analysis of large graphs : State-of-the-art and future research challenges

The analysis of large graphs plays a prominent role in various fields of research and is relevant in many important application areas. Effective visual analysis of graphs requires appropriate visual presentations in combination with respective user interaction facilities and algorithmic graph analysis methods. How to design appropriate graph analysis systems depends on many factors, including the type of graph describing the data, the analytical task at hand and the applicability of graph analysis methods. The most recent surveys of graph visualization and navigation techniques cover techniques that had been introduced until 2000 or concentrate only on graph layouts published until 2002. Recently, new techniques have been developed covering a broader range of graph types, such as time‐varying graphs. Also, in accordance with ever growing amounts of graph‐structured data becoming available, the inclusion of algorithmic graph analysis and interaction techniques becomes increasingly important. In this State‐of‐the‐Art Report, we survey available techniques for the visual analysis of large graphs. Our review first considers graph visualization techniques according to the type of graphs supported. The visualization techniques form the basis for the presentation of interaction approaches suitable for visual graph exploration. As an important component of visual graph analysis, we discuss various graph algorithmic aspects useful for the different stages of the visual graph analysis process. We also present main open research challenges in this field.

The value of visualization

The field of visualization is getting mature. Many problems have been solved, and new directions are sought for. In order to make good choices, an understanding of the purpose and meaning of visualization is needed. Especially, it would be nice if we could assess what a good visualization is. In this paper an attempt is made to determine the value of visualization. A technological viewpoint is adopted, where the value of visualization is measured based on effectiveness and efficiency. An economic model of visualization is presented, and benefits and costs are established. Next, consequences (brand limitations of visualization are discussed (including the use of alternative methods, high initial costs, subjective/less, and the role of interaction), as well as examples of the use of the model for the judgement of existing classes of methods and understanding why they are or are not used in practice. Furthermore, two alternative views on visualization are presented and discussed: viewing visualization as an art or as a scientific discipline. Implications and future directions are identified.

Cushion treemaps: visualization of hierarchical information

A new method is presented for the visualization of hierarchical information, such as directory structures and organization structures. Cushion treemaps inherit the elegance of standard treemaps: compact, space-filling displays of hierarchical information, based on recursive subdivision of a rectangular image space. Intuitive shading is used to provide insight in the hierarchical structure. During the subdivision, ridges are added per rectangle, which are rendered with a simple shading model. The result is a surface that consists of recursive cushions. The method is efficient, effective, easy to use and implement, and has a wide applicability.A new method is presented for the visualization of hierarchical information, such as directory structures and organization structures. Cushion treemaps inherit the elegance of standard treemaps: compact, space-filling displays of hierarchical information, based on recursive subdivision of a rectangular image space. Intuitive shading is used to provide insight in the hierarchical structure. During the subdivision, ridges are added per rectangle, which are rendered with a simple shading model. The result is a surface that consists of recursive cushions. The method is efficient, effective, easy to use and implement, and has a wide applicability.

Interactive Visualization of Small World Graphs

Many real world graphs have small world characteristics, that is, they have a small diameter compared to the number of nodes and exhibit a local cluster structure. Examples are social networks, software structures, bibliographic references and biological neural nets. Their high connectivity makes both finding a pleasing layout and a suitable clustering hard. In this paper we present a method to create scalable, interactive visualizations of small world graphs, allowing the user to inspect local clusters while maintaining a global overview of the entire structure. The visualization method uses a combination of both semantical and geometrical distortions, while the layout is generated by a spring embedder algorithm using recently developed force model. We use a cross referenced database of 500 artists as a running example

Evaluation of fiber clustering methods for diffusion tensor imaging

Fiber tracking is a standard approach for the visualization of the results of diffusion tensor imaging (DTI). If fibers are reconstructed and visualized individually through the complete white matter, the display gets easily cluttered making it difficult to get insight in the data. Various clustering techniques have been proposed to automatically obtain bundles that should represent anatomical structures, but it is unclear which clustering methods and parameter settings give the best results. We propose a framework to validate clustering methods for white-matter fibers. Clusters are compared with a manual classification which is used as a ground truth. For the quantitative evaluation of the methods, we developed a new measure to assess the difference between the ground truth and the clusterings. The measure was validated and calibrated by presenting different clusterings to physicians and asking them for their judgement. We found that the values of our new measure for different clusterings match well with the opinions of physicians. Using this framework, we have evaluated different clustering algorithms, including shared nearest neighbor clustering, which has not been used before for this purpose. We found that the use of hierarchical clustering using single-link and a fiber similarity measure based on the mean distance between fibers gave the best results.

Bridging the gaps

Users play a central role in visualization. The ultimate aim of visualization is to provide insight to users, not just to produce images. Since the late 1980s, our field has spent much effort on developing new methods to help users obtain insight, and we have made a lot of progress. Many researchers nowadays use visualization routinely to understand the results of their measurements and simulations. However, many problems still exist, and not every method reaches its intended audience. In recent years, discussion has focused on the position of our field and which goals to pursue. In this article, the author considers the issue from the perspective of the day-to-day practice of academic visualization research

A probe for local flow field visualization

A probe for the interactive visualization of flow fields is presented. The probe can be used to visualize many characteristics of the flow in detail for a small region in the data set. The velocity and the local change of velocity (the velocity gradient tensor) are visualized by a set of geometric primitives. To this end, the velocity gradient tensor is transformed to a local coordinate frame, and decomposed into components parallel with and perpendicular to the flow. These components are visualized as geometric objects with an intuitively meaningful interpretation. An implementation is presented which shows that this probe is a useful tool for flow visualization.A probe for the interactive visualization of flow fields is presented. The probe can be used to visualize many characteristics of the flow in detail for a small region in the data set. The velocity and the local change of velocity (the velocity gradient tensor) are visualized by a set of geometric primitives. To this end, the velocity gradient tensor is transformed to a local coordinate frame, and decomposed into components parallel with and perpendicular to the flow. These components are visualized as geometric objects with an intuitively meaningful interpretation. An implementation is presented which shows that this probe is a useful tool for flow visualization. >

Image based flow visualization for curved surfaces

A new method for the synthesis of dense, vector-field aligned textures on curved surfaces is presented, called IBFVS. The method is based on image based flow visualization (IBFV). In IBFV two-dimensional animated textures are produced by defining each frame of a flow animation as a blend between a warped version of the previous image and a number of filtered white noise images. We produce flow aligned texture on arbitrary three-dimensional triangular meshes in the same spirit as the original method: texture is generated directly in image space. We show that IBFVS is efficient and effective. High performance (typically fifty frames or more per second) is achieved by exploiting graphics hardware. Also, IBFVS can easily be implemented and a variety of effects can be achieved. Applications are flow visualization and surface rendering. Specifically, we show how to visualize the wind field on the earth and how to render a dirty bronze bunny.

Understanding Execution Traces Using Massive Sequence and Circular Bundle Views

The use of dynamic information to aid in software understanding is a common practice nowadays. One of the many approaches concerns the comprehension of execution traces. A major issue in this context is scalability: due to the vast amounts of information, it is a very difficult task to successfully find your way through such traces without getting lost. In this paper, we propose the use of a novel trace visualization method based on a massive sequence and circular bundle view, constructed with scalability in mind. By means of three usage scenarios that were conducted on three different software systems, we show how our approach, implemented in a tool called EXTRAVIS, is applicable to the areas of trace exploration, feature location, and feature comprehension.