A multiple view system uses two or more distinct views to support the investigation of a single conceptual entity. Many such systems exist, ranging from computer-aided design (CAD) systems for chip design that display both the logical structure and the actual geometry of the integrated circuit to overview-plus-detail systems that show both an overview for context and a zoomed-in-view for detail. Designers of these systems must make a variety of design decisions, ranging from determining layout to constructing sophisticated coordination mechanisms. Surprisingly, little work has been done to characterize these systems or to express guidelines for their design. Based on a workshop discussion of multiple views, and based on our own design and implementation experience with these systems, we present eight guidelines for the design of multiple view systems. Keywords Multiple views, information visualization, design guidelines, usability heuristics, user interfaces INTRODUCTION Multiple v...

Visualization has proven to be a powerful and widely-applicable tool for the analysis and interpretation of multi-variate data. Most visualization algorithms aim to find a projection from the data space down to a two-dimensional visualization space. However, for complex data sets living in a high-dimensional space it is unlikely that a single two-dimensional projection can reveal all of the interesting structure. We therefore introduce a hierarchical visualization algorithm which allows the complete data set to be visualized at the top level, with clusters and sub-clusters of data points visualized at deeper levels. The algorithm is based on a hierarchical mixture of latent variable models, whose parameters are estimated using the expectation-maximization algorithm. We demonstrate the principle of the approach on a toy data set, and we then apply the algorithm to the visualization of a synthetic data set in 12 dimensions obtained from a simulation of multi-phase flows in oil pipelines,...

This article is intended to be the published standard reference to the XGobi system. The article gives an overview of the functionality of the system as well as a discussion

this article. Section 2 gives an overview of how a user operates the XGvis system. Section 3 deals with algorithm animation, direct manipulation and perturbation of the con guration. Section 4 gives details about the cost functions and their interactively controlled parameters for transformation, subsetting and weighting of dissimilarities. Section 5 describes diagnostics for MDS. Section 6 is about computational and systems aspects, including coordination of windows, algorithms, and large data problems. Finally, Section 7 gives a tour of applications with examples of proximity analysis, dimension reduction, and graph layout in two and more dimensions

In current windowing environments, individual windows are treated independently, making it difficult for users to coordinate information across multiple windows. While coordinated multi-window strategies are increasingly used in visualization and web user interfaces, designs are inflexible and haphazard. The space of such linkedwindow strategies is not well understood and largely unexplored. This paper presents a taxonomy of coordinations, identifies important components, and reviews example interfaces. This 2x3 taxonomy provides guidelines for designers of applications, user interface toolkits, and window managers. We hope to encourage construction of generalized, end-user programmable, robust, multiple-window coordination capabilities. KEYWORDS User Interface, Coordination, Taxonomy, Multiple Window Strategies, Information Visualization,Window Management INTRODUCTION Users are dealing with increasing quantity, variety, and interrelated-ness of information. User tasks are becoming...

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. 1

The term ‘proximity data ’ refers to data sets within which it is possible to assess the similarity of pairs of objects. Multidimensional scaling (MDS) is applied to such data and attempts to map high-dimensional objects onto low-dimensional space through the preservation of these similarity relationships. Standard MDS techniques have in the past suffered from high computational complexity and, as such, could not feasibly be applied to data sets over a few thousand objects in size. Through a novel hybrid approach based upon stochastic sampling, interpolation and spring models, we have designed an algorithm running in O(N÷N). Using Chalmers ’ 1996 O(N 2) spring model as a benchmark for the evaluation of our technique, we compare layout quality and run times using data sets of synthetic and real data. Our algorithm executes significantly faster than Chalmers ’ 1996 algorithm, whilst producing superior layouts. In reducing complexity and run time, we allow the visualisation of data sets of previously infeasible size. Our results indicate that our method is a solid foundation for interactive and visual exploration of data. 1.

Abstract—Even though interaction is an important part of information visualization (Infovis), it has garnered a relatively low level of attention from the Infovis community. A few frameworks and taxonomies of Infovis interaction techniques exist, but they typically focus on low-level operations and do not address the variety of benefits interaction provides. After conducting an extensive review of Infovis systems and their interactive capabilities, we propose seven general categories of interaction techniques widely used in Infovis: 1) Select, 2) Explore, 3) Reconfigure, 4) Encode, 5) Abstract/Elaborate, 6) Filter, and 7) Connect. These categories are organized around a user’s intent while interacting with a system rather than the low-level interaction techniques provided by a system. The categories can act as a framework to help discuss and evaluate interaction techniques and hopefully lay an initial foundation toward a deeper understanding and a science of interaction. Index Terms—Information visualization, interaction, interaction techniques, taxonomy, visual analytics 1

Projections of high-dimensional data onto low-dimensional subspaces provide insightful views for understanding multivariate relationships. In this paper we discuss how to manually control the variable contributions to the projection. The user has control of the way a particular variable contributes to the viewed projection and can interactively adjust the variable's contribution. These manual controls complement the automatic views provided by a grand tour, or a guided tour, and give greatly improved flexibility to data analysts. 1 Introduction This paper builds on dynamic visualization methods for high-dimensional data using low-dimensional projections. Among these methods, the most familiar are 3-D data rotations, generated by displaying a continuous sequence of 2-D projections of 3-D data. From a statistical perspective it is rare to have data that are strictly 3-D, and so, unlike most computer graphics applications, the more useful methods for data analysis show projections from a...

Information visualizations with multiple coordinated views enable users to rapidly explore complex data and discover relationships. However, it is usually difficult for users to find or create the coordinated visualizations they need. Snap-Together Visualization allows users to coordinate multiple views that are customized to their needs. Users query their relational database and load results into desired visualizations. Then they specify coordinations between visualizations for selecting, navigating, or re-querying. Developers can make independent visualization tools `snapable ' by including a few hooks. KEYWORDS: User Interface, Coordination, Multiple Views, Tightly Coupled, Information Visualization. INTRODUCTION The multiple coordinated views approach is a powerful and increasingly-employed user-interface technique for exploring information. Each view is a visualization of some part of the information, and views are coordinated (a.k.a. "tightly coupled", or "linked") so that t...