IEEE VIS Publication Dataset

Extracting and visualizing temporal patterns in large scientific data is an open problem in visualization research. First, there are few proven methods to flexibly and concisely define general temporal patterns for visualization. Second, with large time-dependent data sets, as typical with todaypsilas large-scale simulations, scalable and general solutions for handling the data are still not widely available. In this work, we have developed a textual pattern matching approach for specifying and identifying general temporal patterns. Besides defining the formalism of the language, we also provide a working implementation with sufficient efficiency and scalability to handle large data sets. Using recent large-scale simulation data from multiple application domains, we demonstrate that our visualization approach is one of the first to empower a concept driven exploration of large-scale time-varying multivariate data.

The method of Moving Least Squares (MLS) is a popular framework for reconstructing continuous functions from scattered data due to its rich mathematical properties and well-understood theoretical foundations. This paper applies MLS to volume rendering, providing a unified mathematical framework for ray casting of scalar data stored over regular as well as irregular grids. We use the MLS reconstruction to render smooth isosurfaces and to compute accurate derivatives for high-quality shading effects. We also present a novel, adaptive preintegration scheme to improve the efficiency of the ray casting algorithm by reducing the overall number of function evaluations, and an efficient implementation of our framework exploiting modern graphics hardware. The resulting system enables high-quality volume integration and shaded isosurface rendering for regular and irregular volume data.

Information visualisation is about gaining insight into data through a visual representation. This data is often multivariate and increasingly, the datasets are very large. To help us explore all this data, numerous visualisation applications, both commercial and research prototypes, have been designed using a variety of techniques and algorithms. Whether they are dedicated to geo-spatial data or skewed hierarchical data, most of the visualisations need to adopt strategies for dealing with overcrowded displays, brought about by too much data to fit in too small a display space. This paper analyses a large number of these clutter reduction methods, classifying them both in terms of how they deal with clutter reduction and more importantly, in terms of the benefits and losses. The aim of the resulting taxonomy is to act as a guide to match techniques to problems where different criteria may have different importance, and more importantly as a means to critique and hence develop existing and new techniques.

Online pick'em games, such as the recent NCAA college basketball March Madness tournament, form a large and rapidly growing industry. In these games, players make predictions on a tournament bracket that defines which competitors play each other and how they proceed toward a single champion. Throughout the course of the tournament, players monitor the brackets to track progress and to compare predictions made by multiple players. This is often a complex sense making task. The classic bracket visualization was designed for use on paper and utilizes an incrementally additive system in which the winner of each match-up is rewritten in the next round as the tournament progresses. Unfortunately, this representation requires a significant amount of space and makes it relatively difficult to get a quick overview of the tournament state since competitors take arbitrary paths through the static bracket. In this paper, we present AdaptiviTree, a novel visualization that adaptively deforms the representation of the tree and uses its shape to convey outcome information. AdaptiviTree not only provides a more compact and understandable representation, but also allows overlays that display predictions as well as other statistics. We describe results from a lab study we conducted to explore the efficacy of AdaptiviTree, as well as from a deployment of the system in a recent real-world sports tournament.

In this paper we investigate the effectiveness of animated transitions between common statistical data graphics such as bar charts, pie charts, and scatter plots. We extend theoretical models of data graphics to include such transitions, introducing a taxonomy of transition types. We then propose design principles for creating effective transitions and illustrate the application of these principles in DynaVis, a visualization system featuring animated data graphics. Two controlled experiments were conducted to assess the efficacy of various transition types, finding that animated transitions can significantly improve graphical perception.

Treemaps provide an interesting solution for representing hierarchical data. However, most studies have mainly focused on layout algorithms and paid limited attention to the interaction with treemaps. This makes it difficult to explore large data sets and to get access to details, especially to those related to the leaves of the trees. We propose the notion of zoomable treemaps (ZTMs), an hybridization between treemaps and zoomable user interfaces that facilitates the navigation in large hierarchical data sets. By providing a consistent set of interaction techniques, ZTMs make it possible for users to browse through very large data sets (e.g., 700,000 nodes dispatched amongst 13 levels). These techniques use the structure of the displayed data to guide the interaction and provide a way to improve interactive navigation in treemaps.

Information visualization has often focused on providing deep insight for expert user populations and on techniques for amplifying cognition through complicated interactive visual models. This paper proposes a new subdomain for infovis research that complements the focus on analytic tasks and expert use. Instead of work-related and analytically driven infovis, we propose casual information visualization (or casual infovis) as a complement to more traditional infovis domains. Traditional infovis systems, techniques, and methods do not easily lend themselves to the broad range of user populations, from expert to novices, or from work tasks to more everyday situations. We propose definitions, perspectives, and research directions for further investigations of this emerging subfield. These perspectives build from ambient information visualization (Skog et al., 2003), social visualization, and also from artistic work that visualizes information (Viegas and Wattenberg, 2007). We seek to provide a perspective on infovis that integrates these research agendas under a coherent vocabulary and framework for design. We enumerate the following contributions. First, we demonstrate how blurry the boundary of infovis is by examining systems that exhibit many of the putative properties of infovis systems, but perhaps would not be considered so. Second, we explore the notion of insight and how, instead of a monolithic definition of insight, there may be multiple types, each with particular characteristics. Third, we discuss design challenges for systems intended for casual audiences. Finally we conclude with challenges for system evaluation in this emerging subfield.

We present a directed acyclic graph visualisation designed to allow interaction with a set of multiple classification trees, specifically to find overlaps and differences between groups of trees and individual trees. The work is motivated by the need to find a representation for multiple trees that has the space-saving property of a general graph representation and the intuitive parent-child direction cues present in individual representation of trees. Using example taxonomic data sets, we describe augmentations to the common barycenter DAG layout method that reveal shared sets of child nodes between common parents in a clearer manner. Other interactions such as displaying the multiple ancestor paths of a node when it occurs in several trees, and revealing intersecting sibling sets within the context of a single DAG representation are also discussed.

We introduce a series of geographically weighted (GW) interactive graphics, or geowigs, and use them to explore spatial relationships at a range of scales. We visually encode information about geographic and statistical proximity and variation in novel ways through gw-choropleth maps, multivariate gw-boxplots, gw-shading and scalograms. The new graphic types reveal information about GW statistics at several scales concurrently. We impement these views in prototype software containing dynamic links and GW interactions that encourage exploration and refine them to consider directional geographies. An informal evaluation uses interactive GW techniques to consider Guerry's dataset of 'moral statistics', casting doubt on correlations originally proposed through visual analysis, revealing new local anomalies and suggesting multivariate geographic relationships. Few attempts at visually synthesising geography with multivariate statistical values at multiple scales have been reported. The geowigs proposed here provide informative representations of multivariate local variation, particularly when combined with interactions that coordinate views and result in gw-shading. We argue that they are widely applicable to area and point-based geographic data and provide a set of methods to support visual analysis using GW statistics through which the effects of geography can be explored at multiple scales.

Understanding how people use online maps allows data acquisition teams to concentrate their efforts on the portions of the map that are most seen by users. Online maps represent vast databases, and so it is insufficient to simply look at a list of the most-accessed URLs. Hotmap takes advantage of the design of a mapping system's imagery pyramid to superpose a heatmap of the log files over the original maps. Users' behavior within the system can be observed and interpreted. This paper discusses the imagery acquisition task that motivated Hotmap, and presents several examples of information that Hotmap makes visible. We discuss the design choices behind Hotmap, including logarithmic color schemes; low-saturation background images; and tuning images to explore both infrequently-viewed and frequently-viewed spaces.

In many domains, increased collaboration has lead to more innovation by fostering the sharing of knowledge, skills, and ideas. Shared analysis of information visualizations does not only lead to increased information processing power, but team members can also share, negotiate, and discuss their views and interpretations on a dataset and contribute unique perspectives on a given problem. Designing technologies to support collaboration around information visualizations poses special challenges and relatively few systems have been designed. We focus on supporting small groups collaborating around information visualizations in a co-located setting, using a shared interactive tabletop display. We introduce an analysis of challenges and requirements for the design of co-located collaborative information visualization systems. We then present a new system that facilitates hierarchical data comparison tasks for this type of collaborative work. Our system supports multi-user input, shared and individual views on the hierarchical data visualization, flexible use of representations, and flexible workspace organization to facilitate group work around visualizations.

Exploratory visual analysis is useful for the preliminary investigation of large structured, multifaceted spatio-temporal datasets. This process requires the selection and aggregation of records by time, space and attribute, the ability to transform data and the flexibility to apply appropriate visual encodings and interactions. We propose an approach inspired by geographical 'mashups' in which freely-available functionality and data are loosely but flexibly combined using de facto exchange standards. Our case study combines MySQL, PHP and the LandSerf GIS to allow Google Earth to be used for visual synthesis and interaction with encodings described in KML. This approach is applied to the exploration of a log of 1.42 million requests made of a mobile directory service. Novel combinations of interaction and visual encoding are developed including spatial 'tag clouds', 'tag maps', 'data dials' and multi-scale density surfaces. Four aspects of the approach are informally evaluated: the visual encodings employed, their success in the visual exploration of the dataset, the specific tools used and the 'mashup' approach. Preliminary findings will be beneficial to others considering using mashups for visualization. The specific techniques developed may be more widely applied to offer insights into the structure of multifarious spatio-temporal data of the type explored here.

Numerous systems have been developed to display large collections of data for urban contexts; however, most have focused on layering of single dimensions of data and manual calculations to understand relationships within the urban environment. Furthermore, these systems often limit the user's perspectives on the data, thereby diminishing the user's spatial understanding of the viewing region. In this paper, we introduce a highly interactive urban visualization tool that provides intuitive understanding of the urban data. Our system utilizes an aggregation method that combines buildings and city blocks into legible clusters, thus providing continuous levels of abstraction while preserving the user's mental model of the city. In conjunction with a 3D view of the urban model, a separate but integrated information visualization view displays multiple disparate dimensions of the urban data, allowing the user to understand the urban environment both spatially and cognitively in one glance. For our evaluation, expert users from various backgrounds viewed a real city model with census data and confirmed that our system allowed them to gain more intuitive and deeper understanding of the urban model from different perspectives and levels of abstraction than existing commercial urban visualization systems.

We describe the design and deployment of Many Eyes, a public Web site where users may upload data, create interactive visualizations, and carry on discussions. The goal of the site is to support collaboration around visualizations at a large scale by fostering a social style of data analysis in which visualizations not only serve as a discovery tool for individuals but also as a medium to spur discussion among users. To support this goal, the site includes novel mechanisms for end-user creation of visualizations and asynchronous collaboration around those visualizations. In addition to describing these technologies, we provide a preliminary report on the activity of our users.

This paper presents a new algorithm for force directed graph layout on the GPU. The algorithm, whose goal is to compute layouts accurately and quickly, has two contributions. The first contribution is proposing a general multi-level scheme, which is based on spectral partitioning. The second contribution is computing the layout on the GPU. Since the GPU requires a data parallel programming model, the challenge is devising a mapping of a naturally unstructured graph into a well-partitioned structured one. This is done by computing a balanced partitioning of a general graph. This algorithm provides a general multi-level scheme, which has the potential to be used not only for computation on the GPU, but also on emerging multi-core architectures. The algorithm manages to compute high quality layouts of large graphs in a fraction of the time required by existing algorithms of similar quality. An application for visualization of the topologies of ISP (Internet service provider) networks is presented.

The need to visualize large social networks is growing as hardware capabilities make analyzing large networks feasible and many new data sets become available. Unfortunately, the visualizations in existing systems do not satisfactorily resolve the basic dilemma of being readable both for the global structure of the network and also for detailed analysis of local communities. To address this problem, we present NodeTrix, a hybrid representation for networks that combines the advantages of two traditional representations: node-link diagrams are used to show the global structure of a network, while arbitrary portions of the network can be shown as adjacency matrices to better support the analysis of communities. A key contribution is a set of interaction techniques. These allow analysts to create a NodeTrix visualization by dragging selections to and from node-link and matrix forms, and to flexibly manipulate the NodeTrix representation to explore the dataset and create meaningful summary visualizations of their findings. Finally, we present a case study applying NodeTrix to the analysis of the InfoVis 2004 coauthorship dataset to illustrate the capabilities of NodeTrix as both an exploration tool and an effective means of communicating results.

In interfaces that provide multiple visual information resolutions (VIR), low-VIR overviews typically sacrifice visual details for display capacity, with the assumption that users can select regions of interest to examine at higher VI Rs. Designers can create low VIRs based on multi-level structure inherent in the data, but have little guidance with single-level data. To better guide design tradeoff between display capacity and visual target perceivability, we looked at overview use in two multiple-VIR interfaces with high-VIR displays either embedded within, or separate from, the overviews. We studied two visual requirements for effective overview and found that participants would reliably use the low-VIR overviews only when the visual targets were simple and had small visual spans. Otherwise, at least 20% chose to use the high-VIR view exclusively. Surprisingly, neither of the multiple-VIR interfaces provided performance benefits when compared to using the high-VIR view alone. However, we did observe benefits in providing side-by-side comparisons for target matching. We conjecture that the high cognitive load of multiple-VIR interface interactions, whether real or perceived, is a more considerable barrier to their effective use than was previously considered.

This paper presents scented widgets, graphical user interface controls enhanced with embedded visualizations that facilitate navigation in information spaces. We describe design guidelines for adding visual cues to common user interface widgets such as radio buttons, sliders, and combo boxes and contribute a general software framework for applying scented widgets within applications with minimal modifications to existing source code. We provide a number of example applications and describe a controlled experiment which finds that users exploring unfamiliar data make up to twice as many unique discoveries using widgets imbued with social navigation data. However, these differences equalize as familiarity with the data increases.

Documents and other categorical valued time series are often characterized by the frequencies of short range sequential patterns such as n-grams. This representation converts sequential data of varying lengths to high dimensional histogram vectors which are easily modeled by standard statistical models. Unfortunately, the histogram representation ignores most of the medium and long range sequential dependencies making it unsuitable for visualizing sequential data. We present a novel framework for sequential visualization of discrete categorical time series based on the idea of local statistical modeling. The framework embeds categorical time series as smooth curves in the multinomial simplex summarizing the progression of sequential trends. We discuss several visualization techniques based on the above framework and demonstrate their usefulness for document visualization.

This paper describes Show Me, an integrated set of user interface commands and defaults that incorporate automatic presentation into a commercial visual analysis system called Tableau. A key aspect of Tableau is VizQL, a language for specifying views, which is used by Show Me to extend automatic presentation to the generation of tables of views (commonly called small multiple displays). A key research issue for the commercial application of automatic presentation is the user experience, which must support the flow of visual analysis. User experience has not been the focus of previous research on automatic presentation. The Show Me user experience includes the automatic selection of mark types, a command to add a single field to a view, and a pair of commands to build views for multiple fields. Although the use of these defaults and commands is optional, user interface logs indicate that Show Me is used by commercial users.