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.

In this paper we describe the VisDB system, which allows an exploration of large databases using visualization techniques. The goal of the system is to support the query specification process by using each pixel of the display to represent one data item of the database. By arranging and coloring the pixels according to the relevance of the data items with respect to the query, the user gets a visual impression of the resulting data set. Using sliders for each condition of the query, the user may change the query dynamically and receives immediate feedback from the visual representation of the resulting data set. Different visualization techniques are available for different stages of exploration. The first technique uses multiple windows for the different query parts, providing visual feedback for each part of the query and helping the user to understand the overall result. The second technique is an extension of the first one, providing additional information by assignin...

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Rivet is a visualization system for the study of complex computer systems. Since computer systems analysis and visualization is an unpredictable and iterative process, a key design goal of Rivet is to support the rapid development of interactive visualizations capable of visualizing large data sets. In this paper, we present Rivet's architecture, focusing on its support for varied data sources, interactivity, composition and user-defined data transformations. We also describe the challenges of implementing this architecture efficiently and flexibly. We conclude with several examples of computer systems visualizations generated within Rivet, including studies of parallel systems, superscalar processors, and mobile network usage. 1

We introduce an approach to visual analysis of multivariate data that integrates several methods from information visualization, exploratory data analysis (EDA), and geovisualization. The approach leverages the component-based architecture implemented in GeoVISTA Studio to construct a flexible, multiview, tightly (but generically) coordinated, EDA toolkit. This toolkit builds upon traditional ideas behind both small multiples and scatterplot matrices in three fundamental ways. First, we develop a general, MultiForm, Bivariate Matrix and a complementary MultiForm, Bivariate Small Multiple plot in which different bivariate repre- sentation forms can be used in combination. We demonstrate the flexibility of this approach with matrices and small multiples that depict multivariate data through combinations of: scatterplots, bivariate maps, and space-filling displays. Second, we apply a measure of conditional entropy to (a) identify variables from a high-dimensional data set that are likely to display interesting relationships and (b) generate a default order of these variables in the matrix or small multiple display. Third, we add conditioning, a kind of dynamic query/filtering in which supplementary (undisplayed) variables are used to constrain the view onto variables that are displayed. Conditioning allows the effects of one or more well understood variables to be removed from the analysis, making relationships among remaining variables easier to explore. We illustrate the individual and combined functionality enabled by this approach through application to analysis of cancer diagnosis and mortality data and their associated covariates and risk factors.

In this short essay, I consider some relationships between the integrated cognitive-semiotic approach to cartographic representation that I develop in How Maps Work and Jacques Bertin's contributions to cartographic and graphic theory. I relate my approach to his comprehensive semiologic framework for map and graphic design as well as his efforts toward a structured method of graphic information processing. In relation to the former, particular attention is given to critiques of Bertin's "graphic variables" and work that extends them into the domains of time, sound, and touch. Then, the commonalities between his concept of geographic information processing and recent developments in geographic visualization (geovisualization) are outlined. Following from the latter, three geovisualization research challenges for the coming decade are highlighted: (1) developing a typology of operations for interactive georepresentations and a syntactics for their use; (2) balancing abstraction and realism in GeoVirtual environments; and (3) facilitating different place collaboration. keywords: geographic visualization, dynamic cartography, interactivity, animation, semiotics, cognition, virtual environments

Modern computer systems routinely present information to the user as a combination of text and diagrammatic images, described as "graphical user interfaces". Practitioners and researchers in Human-Computer Interaction (HCI) generally believe that the value of these diagrammatic representations is derived from metaphorical reasoning; they communicate abstract information by depicting a physical situation from which the abstractions can be inferred. This assumption has been prevalent in HCI research for over 20 years, but has seldom been tested experimentally. This thesis analyses the reasons why diagrams are believed to assist with abstract reasoning. It then presents the results of a series of experiments testing the contribution of metaphor to comprehension, problem solving, explanation and memory tasks carried out using a range of different diagrams. The results indicate that explicit metaphors provide surprisingly little benefit for cognitive tasks using diagrams as an external re...

Software visualization is nifty stuff; but is it the powerful cognitive tool it is often assumed to be? This chapter attempts to moderate the understandable enthusiasm for software visualization and to raise some of the questions for which the discipline doesn't yet have answers. The chapter is structured as a list of questions with discussion. The questions are not a comprehensive analysis of cognitive challenges in software visualization. Rather, the chapter attempts to provide a list sufficiently provocative to give designers pause, in order: (a) to establish that good software visualization isn't simply a matter of mimicking paper-based tasks or doing what is technically easy---and certainly isn't `solved' yet; but also (b) even simple tools can improve software comprehension, if they're the right ones.

In this paper, we present an evolving system for the analysis and visualization of parallel application performance on shared memory multiprocessors. Our system couples SimOS, a complete machine simulator, with Rivet, a powerful visualization environment. This system demonstrates how visualization is necessary to realize the full power of simulation for performance analysis. We identify several features required of the visualization system, including flexibility, exploratory interaction techniques, and data aggregation schemes. We demonstrate the effectiveness of this parallel analysis and visualization system with a case study. We developed two visualizations within Rivet to study the Argus parallel rendering library, focusing on the memory system and process scheduling activity of Argus respectively. Using these visualizations, we uncovered several unexpected interactions between Argus and the underlying operating system. The results of the analysis led to changes that greatly improved its performance and scalability. Argus had previously been unable to scale beyond 26 processors; after analysis and modification, it achieved linear speedup up to 45 processors.

Effective multimodal graphing tools can be beneficial to both sighted and visually impaired students and scientists. However, before this can become a reality, considerable research is required on the auditory graphing components. We suggest mappings, polarities, scaling, context, and training be studied in particular. We point to previous work in these areas and make suggestions for expanded research questions. We recommend that more complex and realistic data sets be used, and that visually impaired participants play a larger role in the research. The design of multimodal graphing software should be informed by empirical findings. Effective research and useful software tools will bring a broader perspective to data analysis for all who use graphs, regardless of visual ability. 1.