IEEE VIS Publication Dataset

The nature of an information visualization can be considered to lie in the visual metaphors it uses to structure information. The process of understanding a visualization therefore involves an interaction between these external visual metaphors and the user's internal knowledge representations. To investigate this claim, we conducted an experiment to test the effects of visual metaphor and verbal metaphor on the understanding of tree visualizations. Participants answered simple data comprehension questions while viewing either a treemap or a node-link diagram. Questions were worded to reflect a verbal metaphor that was either compatible or incompatible with the visualization a participant was using. The results suggest that the visual metaphor indeed affects how a user derives information from a visualization. Additionally, we found that the degree to which a user is affected by the metaphor is strongly correlated with the user's ability to answer task questions correctly. These findings are a first step towards illuminating how visual metaphors shape user understanding, and have significant implications for the evaluation, application, and theory of visualization.

We introduce the Word Tree, a new visualization and information-retrieval technique aimed at text documents. A Word Tree is a graphical version of the traditional "keyword-in-context" method, and enables rapid querying and exploration of bodies of text. In this paper we describe the design of the technique, along with some of the technical issues that arise in its implementation. In addition, we discuss the results of several months of public deployment of word trees on Many Eyes, which provides a window onto the ways in which users obtain value from the visualization.

In common Web-based search interfaces, it can be difficult to formulate queries that simultaneously combine temporal, spatial, and topical data filters. We investigate how coordinated visualizations can enhance search and exploration of information on the World Wide Web by easing the formulation of these types of queries. Drawing from visual information seeking and exploratory search, we introduce VisGets - interactive query visualizations of Web-based information that operate with online information within a Web browser. VisGets provide the information seeker with visual overviews of Web resources and offer a way to visually filter the data. Our goal is to facilitate the construction of dynamic search queries that combine filters from more than one data dimension. We present a prototype information exploration system featuring three linked VisGets (temporal, spatial, and topical), and used it to visually explore news items from online RSS feeds.

Wikipedia is an example of the collaborative, semi-structured data sets emerging on the Web. These data sets have large, non-uniform schema that require costly data integration into structured tables before visualization can begin. We present Vispedia, a Web-based visualization system that reduces the cost of this data integration. Users can browse Wikipedia, select an interesting data table, then use a search interface to discover, integrate, and visualize additional columns of data drawn from multiple Wikipedia articles. This interaction is supported by a fast path search algorithm over DBpedia, a semantic graph extracted from Wikipedia's hyperlink structure. Vispedia can also export the augmented data tables produced for use in traditional visualization systems. We believe that these techniques begin to address the "long tail" of visualization by allowing a wider audience to visualize a broader class of data. We evaluated this system in a first-use formative lab study. Study participants were able to quickly create effective visualizations for a diverse set of domains, performing data integration as needed.

Ranking data, which result from m raters ranking n items, are difficult to visualize due to their discrete algebraic structure, and the computational difficulties associated with them when n is large. This problem becomes worse when raters provide tied rankings or not all items are ranked. We develop an approach for the visualization of ranking data for large n which is intuitive, easy to use, and computationally efficient. The approach overcomes the structural and computational difficulties by utilizing a natural measure of dissimilarity for raters, and projecting the raters into a low dimensional vector space where they are viewed. The visualization techniques are demonstrated using voting data, jokes, and movie preferences.

In the established procedural model of information visualization, the first operation is to transform raw data into data tables. The transforms typically include abstractions that aggregate and segment relevant data and are usually defined by a human, user or programmer. The theme of this paper is that for video, data transforms should be supported by low level computer vision. High level reasoning still resides in the human analyst, while part of the low level perception is handled by the computer. To illustrate this approach, we present Viz-A-Vis, an overhead video capture and access system for activity analysis in natural settings over variable periods of time. Overhead video provides rich opportunities for long-term behavioral and occupancy analysis, but it poses considerable challenges. We present initial steps addressing two challenges. First, overhead video generates overwhelmingly large volumes of video impractical to analyze manually. Second, automatic video analysis remains an open problem for computer vision.

Surveys and opinion polls are extremely popular in the media, especially in the months preceding a general election. However, the available tools for analyzing poll results often require specialized training. Hence, data analysis remains out of reach for many casual computer users. Moreover, the visualizations used to communicate the results of surveys are typically limited to traditional statistical graphics like bar graphs and pie charts, both of which are fundamentally noninteractive. We present a simple interactive visualization that allows users to construct queries on large tabular data sets, and view the results in real time. The results of two separate user studies suggest that our interface lowers the learning curve for naive users, while still providing enough analytical power to discover interesting correlations in the data.

Due to the complexity of the patent domain and the huge amount of data, advanced interactive visual techniques are needed to support the analysis of large patent collections and portfolios. In this paper we present a new approach for visualizing the classificatory distribution of patent collections among the International Patent Classification (IPC) - todaypsilas most important internationally agreed patent classification system with about 70.000 categories. Our approach is based on an interactive three-dimensional treemap overlaid with adjacency edge bundles.

Many real-world visual analytics applications involve time-oriented data. I am working in a research project related to this challenge where I am responsible for the interactive visualization part. My goal are interactive visualizations to explore such time-oriented data according to the user tasks while considering the structure of time. Time is composed of many granularities that are likely to have crucial influence on the formation of the data. The challenge is to integrate the granularities into a detailed compound view on the data, like the compound eye of insects integrates many images into one view. Other members of our team are experts in temporal data mining and user centered design. The goal is to combine our research topics to an integrated system that helps domain experts to get more insight from their time-oriented data.

Internet has become one of the best communication and marketing tools. Hence, designing well-structured Web sites with the information or products that users look for is a crucial mission. For this reason, understanding Web data is a decisive task to assure the success of a Website. In that sense, data mining techniques provide many metrics and statistics useful to automatically discover the structure, contents and usage of a site. This research aims at proving the usefulness of a set of information visualisation techniques in order to analyse Web data, using a visual Web mining tool that allows the combination, coordination and exploration of visualisations to get insight on Web data. The tool, named WET, provides a set of visual metaphors that represent the structure of the Websites where Web metrics are overlaid.

This paper introduces the application of visual analytics techniques as a novel approach for improving economic decision making. Particularly, we focus on two known problems where subjectspsila behavior consistently deviates from the optimal, the Winnerpsilas and Loserpsilas Curse. According to economists, subjects fail to recognize the profit-maximizing decision strategy in both the Winnerpsilas and Loserpsilas curse because they are unable to properly consider all the available information. As such, we have created a visual analytics tool to aid subjects in decision making under the Acquiring a Company framework common in many economic experiments. We demonstrate the added value of visual analytics in the decision making process through a series of user studies comparing standard visualization methods with interactive visual analytics techniques. Our work presents not only a basis for development and evaluation of economic visual analytic research, but also empirical evidence demonstrating the added value of applying visual analytics to general decision making tasks.

The Prajna Project is a Java toolkit designed to provide various capabilities for visualization, knowledge representation, geographic displays, semantic reasoning, and data fusion. Rather than attempt to recreate the significant capabilities provided in other tools, Prajna instead provides software bridges to incorporate other toolkits where appropriate. This challenge required the development of a custom application for visual analysis. By applying the utilities within the Prajna project, I developed a robust and diverse set of capabilities to solve the analytical challenge.

MobiVis is a visual analytics tools to aid in the process of processing and understanding complex relational data, such as social networks. At the core of these tools is the ability to filter complex networks structurally and semantically, which helps us discover clusters and patterns in the organization of social networks. Semantic filtering is obtained via an ontology graph, based on another visual analytics tool, called OntoVis. In this summary, we describe how these tools where used to analyze one of the mini-challenges of the 2008 VAST challenge.

In the mobile call mini challenge of VAST 2008 contest, we explored the temporal communication patterns of Catalano/Vidro social network which is reflected in the mobile call data. We focus on detecting the hierarchy of the social network and try to get the important actors in it. We present our tools and methods in this summary. By using the visual analytic approaches, we can find out not only the temporal communication patterns in the social network but also the hierarchy of it.

This article describes the visualization tool developed for analysing a dynamic social network of phone calls, for the VAST 2008 mini challenge. The tool was designed to highlight temporal changes in the network, by animating different network visual representations. We also explain how animating these network representations, helped to identify key events in the mini challenge problem scenario. Finally, we make some suggestions for future research and development in the area.

Insight provenance - a historical record of the process and rationale by which an insight is derived - is an essential requirement in many visual analytics applications. While work in this area has relied on either manually recorded provenance (e.g., user notes) or automatically recorded event-based insight provenance (e.g., clicks, drags, and key-presses), both approaches have fundamental limitations. Our aim is to develop a new approach that combines the benefits of both approaches while avoiding their deficiencies. Toward this goal, we characterize userspsila visual analytic activity at multiple levels of granularity. Moreover, we identify a critical level of abstraction, Actions, that can be used to represent visual analytic activity with a set of general but semantically meaningful behavior types. In turn, the action types can be used as the semantic building blocks for insight provenance. We present a catalog of common actions identified through observations of several different visual analytic systems. In addition, we define a taxonomy to categorize actions into three major classes based on their semantic intent. The concept of actions has been integrated into our labpsilas prototype visual analytic system, HARVEST, as the basis for its insight provenance capabilities.

Visual analytic tools allow analysts to generate large collections of useful analytical results. We anticipate that analysts in most real world situations will draw from these collections when working together to solve complicated problems. This indicates a need to understand how users synthesize multiple collections of results. This paper reports the results of collaborative synthesis experiments conducted with expert geographers and disease biologists. Ten participants were worked in pairs to complete a simulated real-world synthesis task using artifacts printed on cards on a large, paper-covered workspace. Experiment results indicate that groups use a number of different approaches to collaborative synthesis, and that they employ a variety of organizational metaphors to structure their information. It is further evident that establishing common ground and role assignment are critical aspects of collaborative synthesis. We conclude with a set of general design guidelines for collaborative synthesis support tools.

Social network graphs, concept maps, and process charts are examples of diagrammatic representations employed by intelligence analysts to understand complex systems. Unfortunately, these 2D representations currently do not easily convey the flow, sequence, tempo and other important dynamic behaviors within these systems. In this paper we present Configurable Spaces, a novel analytical method for visualizing patterns of activity over time in complex diagrammatically- represented systems. Configurable Spaces extends GeoTime's X, Y, T coordinate workspace space for temporal analysis to any arbitrary diagrammatic work space by replacing a geographic map with a diagram. This paper traces progress from concept to prototype, and discusses how diagrams can be created, transformed and leveraged for analysis, including generating diagrams from knowledge bases, visualizing temporal concept maps, and the use of linked diagrams for exploring complex, multi-dimensional, sequences of events. An evaluation of the prototype by the National Institute of Standards and Technology showed intelligence analysts believed they were able to attain an increased level of insight, were able to explore data more efficiently, and that Configurable Spaces would help them work faster.

USPEX is a crystal structure predictor based on an evolutionary algorithm. Every USPEX run produces hundreds or thousands of crystal structures, some of which may be identical. To ease the extraction of unique and potentially interesting structures we applied usual high-dimensional classification concepts to the unusual field of crystallography. We experimented with various crystal structure descriptors, distinct distance measures and tried different clustering methods to identify groups of similar structures. These methods are already applied in combinatorial chemistry to organic molecules for a different goal and in somewhat different forms, but are not widely used for crystal structures classification. We adopted a visual design and validation method in the development of a library (CrystalFp) and an end-user application to select and validate method choices, to gain userspsila acceptance and to tap into their domain expertise. The use of the classifier has already accelerated the analysis of USPEX output by at least one order of magnitude, promoting some new crystallographic insight and discovery. Furthermore the visual display of key algorithm indicators has led to diverse, unexpected discoveries that will improve the USPEX algorithms.

Software tools that make it easier for analysts to collaborate as a natural part of their work will lead to better analysis that is informed by more perspectives. We are interested to know if software tools can be designed that support collaboration even as they allow analysts to find documents and organize information (including evidence, schemas, and hypotheses). We have modified the Entity Workspace system, described previously, to test such designs. We have evaluated the resulting design in both a laboratory study and a study where it is situated with an analysis team. In both cases, effects on collaboration appear to be positive. Key aspects of the design include an evidence notebook optimized for organizing entities (rather than text characters), information structures that can be collapsed and expanded, visualization of evidence that emphasizes events and documents (rather than emphasizing the entity graph), and a notification system that finds entities of mutual interest to multiple analysts.