The worldwide increase in dementia incidence has important health and environmental implications for both patients and caregivers. The ability to predict who will develop dementia and to identify risk factors of rapid progression would be a major advance in preventative healthcare. Few studies have surveyed the impact of environmental differences in combination with clinical and genetic factors in modifying dementia progression. We examined variation in diagnostic conversion from normal cognition to dementia in a multi-site cohort of study volunteers. Center-to-Center differences, residence in sparsely populated regions, lower education, older age and more severe cognitive impairment at study entry were associated with conversion to dementia within 5 years following the baseline visit. Because of strong individual Center effects, for which the underlying determinants were not identified, studying diagnostic conversion on a Center-by-Center basis may reveal viable genetic and environmental influences.

Alzheimer's Disease (AD) is the 6th leading cause of death in the United States. Although several genetic and environmental factors have been implicated in AD risks, no single factor is found solely responsible for disease manifestation in all Alzheimer's cases. In cases when no single gene can explain causality, the scenario of a combination of factors with modest effect sizes has been suggested. Through means of visual analysis in Weave we uncovered and further strengthened growing evidence for the link between genetics, air pollution and an earlier onset of Alzheimer's disease as exemplified in US cities with the highest reported levels of toxicity. Major sources of bias impacting our dataset are examined to caution the reader about the value of correlation findings in this less than ideal study sample.

Weave (Web-based Analysis and Visualization Environment) facilitates extensive real-time user interaction with visualizations of US Census Bureau data, both geographic and non-geographic. This paper discusses the complexities of preparing a large complex data set for visualization in the tool, and the potential advantages of engaging such data visually. The required pre-processing of the American Community Survey (ACS) data serves as the prime example. The procedure for loading detailed data into Weave is described. Several examples of presenting and interactively manipulating Census data are presented including analytics using R Project supported statistics and how drawing upon Weave's multigeo layering capabilities can support flexibility in implementing normalization options. The development trajectory for weave seeks to expand capabilities for metadata insertion, browsing and searching this rich data source.

InfoMaps is an information visualization tool designed for personal information management and for supporting data analysis. In this paper we briefly discuss the design of InfoMaps and explain its role in finding relevant information. InfoMaps is tightly coupled with Weave, an open source framework, providing a set of data analysis and visualization tools. Weave's framework is built with session states as its core and this provides InfoMaps the ability to store the entire user's interactions as well as visualization layouts. We discuss the implications of using the Weave framework with InfoMaps and its relevance to the field of information retrieval and visual analytics.

Selecting the most expressed dimensions from high dimensional data sets has motivated the design and application of a variety of statistical and machine learning techniques. Here, in our current work, we introduce a metric for assessing the effectiveness of these methods. Our formulation is based on the broad concepts of: (a) devising a formal method of partitioning a visualization's image space; (b) identifying regions that indicate the relative strength of the dimension selection based on how well they are populated by data images; and (c) similarily identifying those regions indicating a poor selection of dimensions. In particular, we explore assessing the quality of radial visualizations. Dimension selection in this class of visualizations strongly effects visualization quality and the sensitivity of cluster formation. We demonstrate the usefulness of Voronoi partitioning the RadViz image space; quantifying radial visualization quality is a direct measure of dimension selection. This work continues to develop and refine the formal theory behind the general class of Normalized Radial Visualizations, including RadViz.

It is often necessary to perform exploratory analysis on datasets in order to determine the patterns to show as presentation visualizations for a target audience. In many cases, exploratory packages do not offer enough flexibility to alter application look and feel in order to produce the required presentations of the data. Runtime modifications of aesthetic, interactive and visualization layering properties allow new interfaces to be defined that can best suit a target audience. Modifications to these properties allow advanced exploratory systems to be reduced down to limited presentation visualizations, and everything in between. This paper explores the design and applications of such runtime application flexibility within the web-based visualization environment Weave.

We introduce a novel interactive visualization technique for semantic networks supporting continuous semantic graph browsing. Our visual semantic graph representation is a nested object visualization representing a view into the graph from a node's viewpoint. In this view, panning and zooming interactions drive graph traversal. We call the visualization technique Fractal Perspective, because it has approximate fractal structure (self similarity at multiple scales) and represents a kind of "perspective projection" from graph space to display space in which distant objects (in graph space) appear smaller (in a dynamic display space). This is work in progress, and this paper should be considered primarily a concept paper with a brief description of a very early prototype harnessing multi-touch interaction.

Current software systems having Ramachandran Plots do not support interactivity with the visualization. Current web-based Ramachandran Plot tools provide only primitive analysis and simple report generation features. In Weave (Web-based Analysis and Visualization Environment), we have developed an improved Ramachandran Plot with interactivity that facilitates better analysis, sequence searching features and supplementary visualizations. Weave provides essential features facilitating broader amino acid analysis, statistical computations and the advantages of a more general web-based application.

Recent research in genomics and biomedical studies has shown a relationship between heredity, mutations in certain genes and the corresponding probabilities of developing certain cancers. In this study we looked at breast and ovarian cancer distributions across all states and counties in the United States over time. We describe briefly Weave, our Web-based Analysis and Visualization Environment and use it to explore these cancers and present interactive animated visualizations of family hereditary patterns and genetic distributions, not only for these cancers, but also for other life-threatening cancers and related health indicators. We also show how Weave can be used to integrate other diverse epidemiological data, in particular obesity, and explore its relationship with cancer data.

The multidisciplinary approach to breast cancer treatment in a comprehensive breast center model can deliver a high standard of care for the patient, but it also generates a wide range of patient data that are typically left as disparate silos, and not organized or analyzed for any clinician to help with patient care. We have developed a method for the synthesis of radiology, surgery, pathology, medical oncology, and genetics information from a patient medical history in a timeline paradigm with elements of clinical decision support and quality indicators to help with decision making. A model for structuring data from the many disparate sources was developed to support enriched probing interactions. Summary views of the current knowledge from each discipline were also tightly integrated. To support the surgical management workflow, the prototype lifelines were integrated into existing clinical software, incorporating all the relevant events, decisions, and data elements.

Graph data mining algorithms rely on graph canonical forms to compare different graph structures. These canonical form definitions depend on node and edge labels. In this paper, we introduce a unique canonical visual matrix representation that only depends on a graph's topological information, so that two structurally identical graphs will have exactly the same visual adjacency matrix representation. In this canonical matrix, nodes are ordered based on a breadth-first search spanning tree. Special rules and filters are designed to guarantee the uniqueness of an arrangement. Such a unique matrix representation provides persistence and a stability which can be used and harnessed in visualization, especially for data exploration and studies.

Lead generation library design is an iterative process of selecting compounds that would make ideal drug candidates. We propose a visual analytics model for effective evaluation of various compound selection strategies. This model incorporates a clustering strategy and a set of evaluation metrics for understanding the current state of knowledge to enable decision making and process customization between iterations.

Clustering techniques are heuristic processes that typically do not yield one optimal solution. Therefore it is common practice to generate multiple cluster sets and investigate the consistency of the record groupings. Fuzzy clustering essentially achieves the same objective by providing a measure of the extent to which each record belongs to each cluster i.e. the strength of association of each record to each cluster. This paper develops a visualization of fuzzy clustering using RadViz, compares it to a Vectorized RadViz visualization of the results of multiple cluster ensembles and then applies it to a microarray dataset.

Security configuration files are created and edited as text files. These files are the essential definition and control of the behavior of security devices. Despite their significant size, complexity, and the possibility of interaction between entries, no visually sophisticated tools exist that explicitly capture and visualize problematic interactions between rules to aid in the comprehension and modification of configuration files. Our initial work on the direct visualization of firewall configurations showed the limitations of visualizing just the range of packets that can be accepted. To visually capture the interactions between rules, we introduce the concept of a "created void." Created voids capture the information about destructive interactions between rules in a firewall ruleset, where an overlap between a deny rule prevents that packet from reaching an accept rule later in the ruleset. We present a lossless five-dimensional visualization of the convex solid decomposition of the set of acceptable packets from a firewall configuration, augmented with visual representations of created voids. This interactive visualization is embedded in a simple firewall ruleset editor, allowing the user to investigate the effect of changes in the ruleset.

Information Visualization (InfoVis) is now an accepted and growing field but questions remain about the best uses for and the maturity of novel visualizations. Usability studies and controlled experiments are helpful but generalization is difficult. We believe that the systematic development of benchmarks will facilitate the comparison of techniques and help identify their strengths under different conditions. We were involved in the organization and management of three information visualization contests for the 2003, 2004 and 2005 IEEE InfoVis Symposia, which requested teams to report on insights gained while exploring data. We give a summary of the state of the art of evaluation in information visualization, describe the three contests, summarize their results, discuss outcomes and lessons learned, and conjecture the future of visualization contests. All materials produced by the contests are archived in the InfoVis Benchmark Repository.

Radviz is a radial visualization with dimensions assigned to points called dimensional anchors (DAs) placed on the circumference of a circle. Records are assigned locations within the circle as a function of its relative attraction to each of the DAs. The DAs can be moved either interactively or algorithmically to reveal different meaningful patterns in the dataset. In this paper we describe Vectorized Radviz (VRV) which extends the number of dimensions through data flattening. We show how VRV increases the power of Radviz through these extra dimensions by enhancing the flexibility in the layout of the DAs. We apply VRV to the problem of analyzing the results of multiple clusterings of the same data set, called multiple cluster sets or cluster ensembles. We show how features of VRV help discern patterns across the multiple cluster sets. We use the Iris data set to explain VRV and a newt gene microarray data set used in studying limb regeneration to show its utility. We then discuss further applications of VRV.

We have studied newt (N. viridescens) gene expression levels in ~ 1200 selected genes important in tissue regeneration at various times post-amputation at 6 different limb amputation sites. Here we provide analyses of microarray data that demonstrate a global gene expression correlation decrease on going from proximal to distal amputation sites of either limb or tail appendages. Also, the proximal (upper) forelimb and hindlimb regenerates have by far the most highly pairwise correlated gene expression levels of all sites. In contrast, the distal (lower) forelimb and hindlimb and tail regenerates reveal the least pairwise correlated gene expression levels. These data support the idea that limb loss at a proximal site produces afar more robust response as compared to a more distal site and requires a greater level of gene regulation to properly rebuild the lost structure.

Since clustering algorithms are heuristic, multiple clustering algorithms applied to the same dataset will typically not generate the same sets of clusters. This is especially true for complex datasets such as those from microarray time series experiments. Two such microarray datasets describing gene expression activities from regenerating newt forelimbs at various times following limb amputation were used in this study. A cluster stability matrix, which shows the number of times two genes appear in the same cluster, was generated as a heat map. This was used to evaluate the overall variation among the clustering algorithms and to identify similar clusters. A comparison of the cluster stability matrices for two related microarray experiments with different levels of precision was shown to be an effective basis for comparing the quality of the two sets of experiments. A pairwise heat map was generated to show which pairs of clustering algorithms grouped the data into similar clusters.

To be most useful, evaluation requires detailed observation and effective analysis of a full spectrum of system use. We have developed an approach and architecture for in-depth data collection and analysis of all use of a visualization system. User interface components in a large visualization and analysis platform automatically record user actions, and can restore previous system states on demand. Audio and text annotations are collected and indexed to states, allowing users to find a comment and restore the system state in which they made it; then explore actions before and after. History is visible as data; so a variety of visual displays and analysis techniques may be used to develop insights about the user’s experience. States of any part of the interface may be analyzed separately. Actions are categorized in a taxonomy as the user interface is built, allowing comparison of similar patterns in all tools. History data can co-exist with other data during data exploration, supporting further individual or group data exploration.

Georges Grinstein Questions often asked when presenting some new model, new theory, new research or new visualization include: How useful or how usable is it? and Have you performed any tests? Visualization is an interface technology and as such includes not just software algorithms and techniques, but computer human interaction issues as well. This makes it draw from both areas, one appearing more focused on utility and the other on usability. One key step in the development of a new theory is the attempt to first solve a problem. That problem or question does not include a section; in some domain it may not even contain a utility one from most people's perspectives. We discuss both sides of the issue to clarify the role of each in the development of new visualization technologies. Position Statement Alfred Kobsa In the HCI literature one can find studies which conclude that ease of use is more important than usefulness (Hubona & Blanton 1996), that the opposite holds true (Liao and Landry 2000), and that ease of use is more important for females while the usefulness is more important for males (Yuen and Ma 2002). In this panel contribution, we will present several user studies with information visualization systems, ranging from lab experiments with closed questions to longitudinal adoption studies with administrative data analysts (Gonzales & Kobsa, 2003; Kobsa 2001, 2003; Mark et al. 2003). -------------------------------------------a e-mail: [email protected] e-mail: [email protected] e-mail: [email protected] e-mail [email protected] e-mail [email protected] Consistent with general HCI research, the results show that both factors are important in certain situations, but do not indicate a clear superiority of one factor over the other. Position Statement Catherine Plaisant Is an airplane a better vehicle than a Jaguar, a mountain bicycle or a kid scooter? It all depends of where you need to go, what your goal for the travel is, how old you are, what terrain you will encounter on the way, how long you can spend learning, and many other parameters. All those vehicles are fairly usable but they all require training except for adults using the scooter, and their utility varies enormously as a function of the task and the user. The average car drivers benefit from years of human factor engineering and a large amount of standardization, allowing them to switch from a pickup truck to a convertible in a snap. Similarly, the success of a visualization tool depends on how well it fits the needs of the users it attempts to serve, and the tasks they want to accomplish. If utility may come first for an expert tool (e.g. for discovery tasks requires days of data examination and manipulation), has to come first in public access information systems that requires immediate usability (e.g. interactive displays of census statistics) otherwise users will walk away frustrated. Usability design principles imply that designers and evaluators understand the needs of users to decide which one of the two utility or comes first, and to set levels of required utility and usability. Utility and are both attainable goals that make each other stronger. Like others, we at the University of Maryland have been developing visualization techniques and have struggled over the years to find the best way to evaluate their benefits. Many evaluations have been controlled experiments and we have found that the most useful evaluations were multi-faceted, including qualitative and quantitative measurements or performance, preference and learnability, and I will be show examples from our research. Often we also find that the observations gathered during the experiment can be as informative as the collected measurements. More recently we have been promoting the development of benchmark datasets and tasks that will allow better comparisons between tools and techniques. We have been involved in the 1st InfoVis contest, which calls for the submission of case studies of pairwise comparison of trees. Three pairs of datasets were provided: philogenies, classifications and file system usage data, 605 Proceedings of the 14th IEEE Visualization Conference (VIS’03) 0-7695-2030-8/03 $ 17.00 © 2003 IEEE and open ended tasks described. Accepted submissions will seed an online repository that can be enhanced over time with additional datasets, tasks, case studies and controlled experiment results. I will report on the results of the contest and reflect on how evaluation repository and benchmark datasets might help us understand how to judge the and utility of our tools. The contest is at: http://www.cs.umd.edu/hcil/iv03contest/ Position Statement Ben Shneiderman The answer to the question of or utility first depends on your definition of usability. For me, is more than the color of widgets and placement of text. Usability is about understanding, stating, and serving user needs. Since these needs are the requirements that shape the tool, they determine the utility. The design of excellent tools depends upon understanding how they will be used; therefore is a pre-requisite for successful utility. Position Statement John T. Stasko If the question is, Which comes first, utility or usability? my answer is yes. Both notions are vitally important in the development of information visualization techniques and systems, and they are just two sides of the same coin. When the field of information visualization formed and first grew, the computer graphics and visualization aspects dominated. More recently, an increasing emphasis on the HCI aspects of the field has emerged as we strive to better understand how people can truly benefit from our ideas. When an information visualization technique is implemented in a system, the component is crucial. Poor interface design can hamper adoption and cloud the utility benefits that may be possible. In information visualization, typically does not equate with the common notion of a system being easy to learn. Information visualization systems are complex and they often will be used extensively for long periods of time. Consequently, making a system efficient and natural to use, making functions and operations visible, and simply paying attention to the user interface are key components. This is especially true in systems where interaction and multiple views are essential. For any information visualization technique to be adopted beyond the initial idea generation, there must be some utility or value in that technique. Our field is not about making pretty pictures. It is about helping people with the complex tasks involved in data analysis and understanding. We need to do a better job of articulating the cognitive tasks that occur in data analysis (location, correlation, emphasis, association, etc.) and articulating how information visualization techniques and systems can help with these tasks. Ultimately, a kind of natural selection will occur: systems with utility and value will be adopted and used, while others will quietly fade away.

Describes data exploration techniques designed to classify DNA sequences. Several visualization and data mining techniques were used to validate and attempt to discover new methods for distinguishing coding DNA sequences (exons) from non-coding DNA sequences (introns). The goal of the data mining was to see whether some other, possibly non-linear combination of the fundamental position-dependent DNA nucleotide frequency values could be a better predictor than the AMI (average mutual information). We tried many different classification techniques including rule-based classifiers and neural networks. We also used visualization of both the original data and the results of the data mining to help verify patterns and to understand the distinction between the different types of data and classifications. In particular, the visualization helped us develop refinements to neural network classifiers, which have accuracies as high as any known method. Finally, we discuss the interactions between visualization and data mining and suggest an integrated approach.

A software architecture is presented to integrate a database management system with data visualization. One of its primary objectives, the retention of user-data interactions, is detailed. By storing all queries over the data along with high-level descriptions of the query results and the associated visualization, the processes by which a database is explored can be analyzed. This approach can lead to important contributions in the development of user models as "data explorers", metadata models for scientific databases, intelligent assistants and data exploration services. We describe the underlying elements of this approach, specifically the visual database exploration model and the metadata objects that support the model.

The paper provides a review of the field of multidimensional data visualisation and discusses some promising methodologies. It considers some crucial problems and directions. The emphasis is more on concepts and foundations rather than ad hoc methods. Visualization is considered as a collection of transformations from problem domains to a perceptual domain, usually visual. The paper discusses the extension of visualisation from the pixel to icons.>

Abstract The reconstruction of a 3D object from its 2D projection(s) and its corresponding problem of 3D object recognition are two of the important research areas in the field of computer vision and artificial intelligence. Reconstruction involves determining the geometric and topological relationship of an object's atomic parts whereas recognition involves identifying an object by some form of template matching. Nagendra and Gujar1 gave a survey of several papers on reconstruction of 3D object from its 2D views. In this paper we present a taxonomy of 3D object reconstruction from 2D projection line drawings. We base the classification on the number of 2D views of the 3D solid object, the degree of user interaction necessary for correct reconstruction, and the internal representation used in the reconstruction process. We discuss the basic issues associated with this problem, review the relevant literature and present topics for future research.

Although the fields of data visualization and automated knowledge discovery (AKD) share many goals, workers in each field have been reluctant to adopt the tools and methods of the other field. Many AKD researchers discourage the use of visualization tools because they believe that dependence on human steering will impede the development of numerical or analytical descriptions of complex data. Many visualization researchers are concerned that their present platforms are being pushed to the limits of their performance by the most advanced visualization techniques and are therefore unwilling to incur the perceived overhead of having a database system mediate access to the data. We argue that these attitudes are somewhat short-sighted and that the techniques of these two communities are complementary. We discuss a specific visualization system that we have developed and describe the obstacles that must be overcome in integrating it into an AKD system. © 1992 John Wiley & Sons, Inc.

An experiment in exploratory data visualization using a massively parallel processor is described. In exploratory data visualization, it is typically not known what is being looked for: instead, the data are explored with a variety of visualization techniques that can illuminate its nature by demonstrating patterns in it. With this approach, the authors were able to find new features in some of their oldest datasets and to create more vivid presentations of familiar features in these datasets. Their experience has also led to a better understanding of the nature of the exploratory visualization and has resulted in some formal representations of the interaction process in this environment.>

The analysis and interpretation of very high dimensional data require the development and use of data presentation techniques that harness human perceptual powers. The University of Lowell's Exploratory Visualization project (Exvis) aims at designing, implementing, and evaluating perceptually-based tools for data presentation using both visual and auditory domains. This paper describes several auditory data presentation techniques, including the generation of stereophonic sound with apparent depth and sound that appears to emanate from a two-dimensional area. Both approaches can produce sound with auditory texture.

This paper presents a reference model for the development of systems for the visualization of multidimensional data. The purpose of the reference model is to build a conceptual basis for thinking about multi-dimensional visualization and for use in developing visualization environments. We describe the reference model in terms of the fundamental concepts of PHIGS (Programmer’s Hierarchical Interactive Graphics System), but extend those concepts to the representation of objects of arbitrary dimensionality.

No abstract available.