An intelligence analyst often needs to keep track of more facts than can be held in human memory. As a result, analysts use a notebook or evidence file to record facts learned so far. In practice, the evidence file is often an electronic document into which text snippets and hand-typed notes are placed. While this kind of evidence file is easy to read and edit, it provides little help for making sense of the captured information. We describe Entity Workspace, a tool designed to be used in place of a traditional evidence file. Entity Workspace combines user interface and entity extraction technologies to build up an explicit model of important entities (people, places, organizations, phone numbers, etc.) and their relationships. Using this model, it helps the analyst find and re-find facts rapidly, notice connections between entities, and identify good documents and entities to explore next.

In this short paper, we summarized recent work in the understanding of social information foraging, and models of how users socially foraging with diverse hints. In communities of practice that depend on foraging in overly rich information environments, there appears to be pressure to self-organize into a balance of some division of labor, plus some degree of cooperation. One way to bridge between different communities of users is to diversify their information sources. We have been examining the possibility of using a collaborative search engine to achieve this effect.

Retyping text phrases can be time consuming. As a result, techniques for copying text from one software application to another, such as copy-and-paste and drag-and-drop are now commonplace. However, even these techniques can be too slow in situations where many phrases need to be copied. In the special case where the phrases to be copied represent syntactically identifiable entities, such as person names, company names, telephone numbers, or street addresses, much faster phrase copying is possible. We describe entity quick click, an approach that reduces both the amount of cursor travel and the number of button presses needed to copy a phrase.

The wide availability of digital reading material online is causing a major shift in everyday reading activities. Readers are increasingly skimming instead of reading in depth. Highlights are increasingly used in digital interfaces to direct attention toward relevant passages within texts. In this paper, we study the eye-tracking behavior of subjects using both keyword highlighting and a new highlighting technique called ScentHighlights, introduced recently [7]. In this first eye-tracking study of highlighting interfaces, we show that there is direct evidence of the von Restorff isolation effect [21] in the eye-tracking data, in that subjects performed better when a fact is isolated (highlighted) against a homogeneous background. Users with the ScentHighlights condition paid more attention to highlighted areas and are more accurate than with other interfaces. In addition to confirming the von Restorff effect, we found that there is great variation in subject differences in reading strategies among subjects, even in the presence of strong cues such as highlights. Some readers scan for highly profitable regions first, while others read sequentially despite the presence of strong highlight cues. The results point to future design possibilities in highlighting interfaces.

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The development of sports throughout history has been essential to our health and keeping us strong physically, mentally, and spiritually. Sport, as a complex human endeavor, promotes unity and brings the world together through the Olympic Games. As an intrinsic part of our social fabric, the various sports in the Olympics are fraught with rules and regulations, and, of course, controversy. The acceptance of technology in sports in the recent past is littered with controversy. Technological developments in sports equipment must therefore take into account of social acceptance issues, such as player perception of fairness and rule changes. In this article, we examine this issue from our experience of introducing a novel wearable sensor system for martial arts.

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Within a more Information Foraging Theory, we have developed a rational analysis of Web use, which has shaped a cognitive model of Web navigation called SNIF-ACT. An automated and practical method for initializing the model with requisite knowledge of information scent was developed based on Pointwise Mutual Information (PMI) computations from a local document corpus with a Web back-off. An automated Web usability tool called Bloodhound was developed that implements an algorithm that approximates the operation of the cognitive model. We report on succesful empirical tests of the SNIF-ACT cognitive mode, the PMI method, and Bloodhound.

Ubiquitous and Wearable Computing both have the goal of pushing the computer into the background, supporting all kinds of human activities. Application areas include areas such as everyday environments (e.g. clothing, home, office), promoting new forms of creative learning via physical/virtual objects, and new tools for interactive design. In this paper, we thrust ubiquitous computing into the extremely hostile environment of the sparring ring of a martial art competition. Our system uses piezoelectric force sensors that transmit signals wirelessly to enable the detection of when a significant impact has been delivered to a competitor’s body. The objective is to support the judges in scoring the sparring matches accurately, while preserving the goal of merging and blending into the background of the activity. The system therefore must take into account of the rules of the game, be responsive in real-time asynchronously, and often cope with untrained operators of the system. We present a user study of the finished prototype and detail our experience.

Our work in the past five years on modeling user actions on the Web has shown that a great deal of information about user actions can be recovered from the informational cues processed by the user during navigation. We call these informational cues by the name of "Information Scent." We have shown in various papers that Information Scent can be used as a methodology for clustering a group of user profiles [Chi02], simulating a collection of users navigating thru the Web with an information need [Chi03], and provid-ing navigational cues to users with transient information goals [Olston03]. We argue in this position paper for a CHI2004 workshop that more research in user profiling should be done for user goals that are transient in nature.

This paper describes the 3Book, a 3D interactive visualization of a codex book as a component for various digital library and sensemaking systems. The book is designed to hold large books and to support sensemaking operations by readers. The book includes methods in which the automatic semantic analysis of the book’s content is used to dynamically tailor access.

This paper describes the 3Book, a 3D interactive visualization of a codex book as a component for digital library and information-intensive applications. The 3Book is able to represent books of almost unlimited length, allows users to read large format books, and has features to enhance reading and sensemaking.

Subject indexes were an important step forward for books because they enabled the comparison and correlations of information without extensive reading, re-reading and memorization. In this short paper, we focus on the user interaction and usage scenario of a new system called ScentIndex that enhances the subject index of an eBook by conceptually reorganizing it to suit particular information needs. Users first enter information needs via keywords describing the concepts they are trying to retrieve and comprehend. ScentIndex then computes what index entries are conceptually related, and reorganizes and displays these index entries on a single page.

In the last 5 years, one major focus of the User Interface Research Group has been understanding how Information Foraging Theory could be used to explain user optimization behavior on the Web. My primary research focus has been on developing the Information Scent Model to describe local behaviors of a group of users around a Web locality. In order to validate this model, we have performed two major validation studies---one on the server end, and the other on the client end: -- We were interested in validating how Information Scent could be used to explain how users will move from page to page by following information cues that are present for each link, and evaluating the probabilities of finding the desired information by clicking through that link. We needed a large amount of user session data that is paired with user goals, and then simulating those goals to see if the user session data match. We performed this study _in situ_ with over 400 subjects at the client end using a remote user logging program, obtaining several thousand usable session data. -- We were also interested in how Information Scent could be used to perform more accurate Web usage data mining. We wanted to construct more accurate user profiles by applying Information Scent principles to Web usage log mining. We have obtained several large scale server logs, of which some sessions are associated with known user goals. We then built user profiles out of these logs. We measured the accuracy of our clustering algorithm by trying to match the user sessions with the known category of the user goals.

The two predominant paradigms for finding information on the Web are browsing and keyword searching. While they exhibit complementary advantages, neither paradigm alone is adequate for complex information goals that lend themselves partially to browsing and partially to searching. To integrate browsing and searching smoothly into a single interface, we introduce a novel approach called ScentTrails. Based on the concept of information scent developed in the context of information foraging theory, ScentTrails highlights hyperlinks to indicate paths to search results. This interface enables users to interpolate smoothly between searching and browsing to locate content matching complex information goals effectively. In a preliminary user study, ScentTrails enabled subjects to find information more quickly than by either searching or browsing alone.

According to usability experts, the top user issue for Web sites is difficult navigation. We have been developing auto-mated usability tools for several years, and here we describe a prototype service called InfoScent™ Bloodhound Simula-tor, a push-button navigation analysis system, which auto-matically analyzes the information cues on a Web site to produce a usability report. We further build upon previous algorithms to create a method called Information Scent Absorption Rate, which measures the navigability of a site by computing the probability of users reaching the desired destinations on the site. Lastly, we present a user study involving 244 subjects over 1385 user sessions that show how Bloodhound correlates with real users surfing for in-formation on four Web sites. The hope is that, by using a simulation of user surfing behavior, we can reduce the need for human labor during usability testing, thus dramatically lower testing costs, and ultimately improving user experience. The Bloodhound Project is unique in that we apply a concrete HCI theory directly to a real-world prob-lem. The lack of empirically validated HCI theoretical model has plagued the development of our field, and this is a step toward that direction.

Web Usage Mining enables new understanding of user goals on the Web. This understanding has broad applications, and traditional mining techniques such as association rules have been used in business applications. We have developed an automated method to directly infer the major groupings of user traffic on a Web site [Heer01]. We do this by utilizing multiple data features in a clustering analysis. We have performed an extensive, systematic evaluation of the proposed approach, and have discovered that certain clustering schemes can achieve categorization accuracies as high as 99% [Heer02b]. In this paper, we describe the further development of this work into a prototype service called LumberJack, a push-button analysis system that is both more automated and accurate than past systems.

Visualization can be viewed as a process that transforms raw data (value) into views. There has been two major category of data process models that have been proposed to model the visualization transformation process. This paper seeks to compare the Data Flow Models and the Data State Models. Specifically, it proves that, in terms of expressiveness, anything that can represented using the Data Flow Model can also be represented using the Data State Model, and vice versa.

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Predictive Web usage visualizations can help analysts uncover traffic patterns and usability problems.

Information visualization is the design and creation of interactive graphic depictions of information by combining principles in the disciplines of graphic design, cognitive science, and interactive computer graphics. This book describes a framework to make information visualization systems easier to develop through the creation of a reference model. It develops and discusses the general utility of this Data State Model, and validates it by applying it to various visualization techniques and showing several systems that illustrate issues such as how to model operators and interactions in visualization systems. The book also applies this reference model to make information visualization more accessible to potential users by creating a `Visualization Spreadsheet', where each cell can contain an entire set of data represented using interactive graphics.

Recent research has explored web user session clustering as a means of understanding user activity and interests on the World Wide Web. Though the proposed techniques have proven to be useful and effective, they require that one either specify the number of clusters in advance or browse a large hierarchy of clusters to find the optimal depth at which to describe user activity. In this paper, we examine the utility of a stability-based technique for automatically determining the optimal number of clusters in the context of web user session clustering. We present two case studies evaluating the technique’s effectiveness.

On the Web, users typically forage for information by navigating from page to page along Web links. Their surfing patterns or actions are guided by their information needs. Researchers need tools to explore the complex interactions between user needs, user actions, and the structures and contents of the Web. In this paper, we describe two computational methods for understanding the relationship between user needs and user actions. First, for a particular pattern of surfing, we seek to infer the associated information need. Second, given an information need, and some pages as starting points, we attempt to predict the expected surfing patterns. The algorithms use a concept called “information scent”, which is the subjective sense of value and cost of accessing a page based on perceptual cues. We present an empirical evaluation of these two algorithms, and show their effectiveness.

Understanding user behaviors on Web sites enables site owners to make sites more usable, ultimately helping users to achieve their goals more quickly. Accordingly, researchers have devised methods for categorizing user sessions in hopes of revealing user interests. These techniques build user profiles by combining users' navigation paths with other data features, such as page viewing time, hyperlink structure, and page content. Previously, we have presented complex techniques of combining many of these data features to cluster user profiles. In this paper, we introduce a user study and a systematic evaluation of these different data features and their associated weighting schemes. We present the results of our study, including accuracy measures for a number of clustering approaches, and offer recommendations for Web analysts. While further investigation over more sites is needed to definitively settle on a robust scheme, we have characterized this analytic space.

On the Web, users typically forage for information by navigating from page to page along Web links. Their surfing patterns or actions are guided by their information needs. Researchers need tools to explore the complex interactions between user needs, user actions, and the structures and contents of the Web. In this paper, we describe two computational methods for understanding the relationship between user needs and user actions. First, for a particular pattern of surfing, we seek to infer the associated information need. Second, given an information need, and some pages as starting points, we attempt to predict the expected surfing patterns. The algorithms use a concept called “information scent”, which is the subjective sense of value and cost of accessing a page based on perceptual cues. We present an empirical evaluation of these two algorithms, and show their effectiveness.

The complexity of software has continuously risen since the invention of the computer, and while Moore's law predicts the growth in processor speed, it fails to take into account our ability in managing complex software processes. Our utilization of the increase in processor speed is very much dependent on our ability to manage this complexity.

Previously, we presented a system called AlignmentViewer that uses information visualization techniques to visualize similarities between a single DNA sequence and a large database of other sequences. In this paper, we extend, summarize, and describe the system using several interesting case studies. We present our comb glyph technique for visualizing alignments between sequences. In this paper, we also extend the original system by incorporating computational steering, and the visualization of differences between data sets. The case studies and the new extended system present our novel approach of extracting significant relationships in the biological data set.

In previous work, researchers have attempted to construct taxonomies of information visualization techniques by examining the data domains that are compatible with these techniques. This is useful because implementers can quickly identify various techniques that can be applied to their domain of interest. However, these taxonomies do not help the implementers understand how to apply and implement these techniques. In this paper, we will extend and then propose a new way to taxonomize information visualization techniques by using the Data State Model. In fact, as the taxonomic analysis in this paper will show, many of the techniques share similar operating steps that can easily be reused. The paper shows that the Data State Model not only helps researchers understand the space of design, but also helps implementers understand how information visualization techniques can be applied more broadly.

Visual computational steering environments extend traditional visualization environments by enabling the user to interactively steer the computations applied to the data. In this paper, we develop a new type of computational steering. "Resource steering" extends current visual steering techniques by providing machine resource estimation and control to the user. With resource steering, the user controls the execution of the computation on a parallel or distributed computer based on experimentally or theoretically derived estimates of the parallel performance of the computation. We demonstrate this extended steering model by applying it to an information visualization system that analyzes genetic sequence similarity reports. We show how our extended steering model enhances the user's ability to control visualization computations.

Digital books can significantly enhance the reading experience, providing many functions not available in printed books. In this paper we study a particular augmentation of digital books that provides readers with customized recommendations. We systematically explore the application of spreading activation over text and citation data to generate useful recommendations. Our findings reveal that for the tasks performed in our corpus, spreading activation over text is more useful than citation data. Further, fusing text and citation data via spreading activation results in the most useful recommendations. The fused spreading activation techniques outperform traditional text-based retrieval methods. Finally, we introduce a preliminary user interface for the display of recommendations from these algorithms.

Designers and researchers of users' interactions with the World Wide Web need tools that permit the rapid exploration of hypotheses about complex interactions of user goals, user behaviors, and Web site designs. We present an architecture and system for the analysis and prediction of user behavior and Web site usability. The system integrates research on human information foraging theory, a reference model of information visualization and Web data-mining techniques. The system also incorporates new methods of Web site visualization (Dome Tree, Usage Based Layouts), a new predictive modeling technique for Web site use (Web User Flow by Information Scent, WUFIS), and new Web usability metrics.

In the process of knowledge discovery, workers examine available information in order to make sense of it. By sensemaking, we mean interacting with and operating on the information with a variety of information processing mechanisms [3, 18]. Previously, we introduced a concept that uses the spreadsheet metaphor with cells containing visualizations of complex data. In this paper, we extend and apply a cognitive model called "visual sensemaking" to the Visualization Spreadsheet. We use the task of making sense of a large Web site as a concrete example throughout the paper for demonstration. Using a variety of visualization techniques, such as the Disk Tree and Cone Tree, we show that the interactions of the Visualization Spreadsheet help users draw conclusions from the overall relationships of the entire information set.

In this paper, we present methods in information visualization that apply to the discovery of patterns in World-Wide Web sites. We hope to use techniques of information visualization to help in the organization and categorization of Web sites. We present a detailed case study of using the spreadsheet to analyze the content, usage, and structure of a large Web site. We demonstrate how the visualization spreadsheet principles apply in this specific data domain.

Information has become interactive. Information visualization is the design and creation of interactive graphic depictions of information by combining principles in the disciplines of graphic design, cognitive science and interactive computer graphics. As the volume and complexity of the data increases, users require more powerful visualization tools that allow them to more effectively explore large abstract datasets. This thesis seeks to make information visualization more accessible to potential users by creating a "Visualization Spreadsheet", where each cell can contain an entire set of data represented using interactive graphics. Just as a numeric spreadsheet enables exploration of numbers, a visualization spreadsheet enables exploration of visual forms of information. Unlike numerical spreadsheets, which store only simple data elements and formulas in each cell, a cell in the Visualization Spreadsheet can hold an entire abstract data set, selection criteria, viewing specifications, and other information needed for a full-fledged information visualization. Similarly, intra-cell and inter-cell operations are far more complex, stretching beyond simple arithmetic and string operations to encompass a range of domain-specific operators. The complexity of operations and interactions requires a visualization framework that is easily understandable to both end-users and visualization designers. This thesis develops and discusses the general utility of a novel visualization framework, and validates the framework by applying it to various visualization techniques and showing several systems that illustrate some of these research issues. We show that the spreadsheet approach facilitates certain visual user tasks that are more difficult using other approaches. The underlying approach in our work allows domain experts to define new data types and data operations and enable visualization experts to incorporate new visualizations, viewing parameters, and view operations.

Information visualization encounters a wide variety of different date domains. The visualization community has developed representation methods and interactive techniques. As a community, we have realized that the requirements in each domain are often dramatically different. In order to easily apply existing methods, researchers have developed a semiology of graphic representation. We have extended this research into a framework that includes operators and interactions in visualization systems, such as a visualization spreadsheet. We discuss properties of this framework and use it to characterize operations spanning a variety of different visualization techniques. The framework developed in this paper enables a new way of exploring and evaluating the design space of visualization operators, and helps end-users in their analysis tasks.

Spreadsheets have proven highly successful for interacting with numerical data, such as applying algebraic operations, refining data propagation relationships, manipulating rows or columns, and exploring "what-if" scenarios. Spreadsheet techniques have recently been extended from numeric domains to other domains. Here we present a spreadsheet approach to displaying and exploring information visualizations, with large, abstract, multidimensional data sets that are visually represented in multiple ways. We illustrate how spreadsheet techniques provide a structured, intuitive, and powerful interface for investigating information visualizations. An earlier version of this article appeared in the proceedings of the 1997 Information Visualization Symposium. Here we refocus the discussion to illustrate principles that make the spreadsheet approach powerful. These principles show how we can perform many user tasks easily in the visualization spreadsheet that prove much more difficult using other approaches.

Several visualizations have emerged which attempt to visualize all or part of the World Wide Web. Those visualizations, however, fail to present the dynamically changing ecology of users and documents on the Web. We present new techniques for Web Ecology and Evolution Visualization (WEEV). Disk Trees represent a discrete time slice of the Web ecology. A collection of Disk Tress forms a Time Tube, representing the evolution of the Web over longer periods of time. These visualizations are intended to aid authors and webmasters with the production and organization of content, assist Web surfers making sense of information, and help researchers understand the Web.

In information visualization, as the volume and complexity of the date increases, researchers require more powerful visualization tools that enable them to more effectively explore multidimensional datasets. In this paper, we discuss the general utility of a novel visualization spreadsheet framework. Just as a numerical spreadsheet enables exploration of numbers, a visualization spreadsheet enables exploration of visual forms of information. We show that the spreadsheet approach facilitates certain information visualization tasks that are more difficult using other approaches. Unlike traditional spreadsheets, which store only simple data elements and formulas in each cell, a visualization spreadsheet cell can hold an entire complex data set, selection criteria, viewing specifications, and other information needed for a full-fledged information visualization. Similarly, inter-cell operations are far more complex, stretching beyond simple arithmetic and string operations to encompass a range of domain-specific operators. We have built two prototype systems that illustrate some of these research issues. The underlying approach in our work allows domain experts to define new data types and data operations, and enables visualization experts to incorporate new visualizations, viewing parameters, and view operations.

In information visualization, as the volume and complexity of the data increases, researchers require more powerful visualization tools that allow them to more effectively explore multi-dimensional datasets. In this paper, we show a novel new visualization framework built upon the spreadsheet metaphor, where each cell can contain an entire dataset. Just as a numerical spreadsheet enables exploration of numbers, a visualization spreadsheet enables exploration of visualizations of data. Our prototype spreadsheet enabled users to compare visualizations in cells using the tabular layout. Users can use the spreadsheet to display, manipulate, and explore multiple visual representation techniques for their data. By applying different operations to the cells, we showed how visualization spreadsheets afford the construction of 'what-if' scenarios. The possible set of operations that users can apply consists of animation, filtering, and algebraic operators.

Information visualization faces challenges presented by the need to represent abstract data and the relationships within the data. Previously, we presented a system for visualizing similarities between a single DNA sequence and a large database of other DNA sequences [6]. Similarity algorithms generate similarity information in textual reports that can be hundreds or thousands of pages long. Our original system visualized the most important variables from these reports. However, the biologists we work with found this system so useful they requested visual representations of other variables. We present an enhanced system for interactive exploration of this multivariate data. We identify a larger set of useful variables in the information space. The new system involves more variables, so it focuses on exploring subsets of the data. We present an interactive system allowing mapping of different variables to different axes, incorporating animation using a time-axis, and providing tools for viewing subsets of the data. Detail-on-demand is preserved by hyperlinks to the analysis reports. We present three case studies illustrating the use of these techniques. The combined technique of applying a time axis with a 3D scatter plot and query filters to visualization of biological sequenced similarity data is both powerful and novel.

Biological sequence similarity analysis presents visualization challenges, primarily because of the massive amounts of discrete, multi-dimensional data. Genomic data generated by molecular biologists is analyzed by algorithms that search for similarity to known sequences in large genomic databases. The output from these algorithms can be several thousand pages of text, and is difficult to analyze because of its length and complexity. We developed and implemented a novel graphical representation for sequence similarity search results, which visually reveals features that are difficult to find in textual reports. The method opens new possibilities in the interpretation of this discrete, multi-dimensional data by enabling interactive investigation of the graphical representation.