Wideband displays fill our field of view, creating new opportunities to develop effective visual interfaces. Although multiple monitors are becoming an affordable way to create wideband displays, the resulting seams create gaps in words and divide diagonal lines into nonaligned segments. We present several novel user interface techniques for creating seam-aware applications, proving that vendors need not wait for affordable seamless displays to exploit the potential of wideband displays.

We present a new technique for efficiently computing Degree-of-Interest distributions to inform the visualization of graph-structured data. The technique is independent of the interest distribution used, and enables fluid interaction with very large data sets (over 100,000 nodes).

Although vendors have made multiple-monitor systems for many years, our interfaces have been stuck in a 30-year old windows paradigm focused on displays much smaller than the desktops we use when working with paper. Advances in flat panel displays and graphics cards now enable affordable personal computers with 6-8 monitors and may someday eliminate seams. This paper argues that vendors should be developing wideband visual interfaces that are designed for displays that fill the human visual field. We describe a longitudinal field study of window activity that found that windows almost always filled a typical single monitor display and that subjects occasionally struggled with window thrashing when they needed to work with two or more windows at the same time. Vendors need not wait for affordable seamless wideband displays before addressing these findings. We have implemented several novel user interface techniques for creating seam-aware applications that target wideband displays based on multiple monitors.

In this paper we describe a visualization architecture (AVID) that employs a dynamic model of user interest to support the design and creation of highly responsive, scalable visualizations of hierarchical data. We present evidence of the architecture's efficacy, showcasing dynamic visualizations with near-immediate (<100ms) update times, even on structures of over 100,000 nodes. We discuss how the key concepts used generalize to arbitrary graph structures. Additionally, we present the results of a user study comparing a prototypical visualization built using AVID to a more traditional file-browser interface, showcasing up to 20% improvement in information access times.

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.

This paper describes the visual analysis tool WebQuilt, a web usability logging and visualization system that helps web design teams record and analyze usability tests. The logging portion of WebQuilt unobtrusively gathers clickstream data as users complete specified tasks. This data is then aggregated and presented as an interactive graph, where nodes of the graph are images of the web pages visited, and arrows are the transitions between pages. To aid analysis of the gathered usability test data, the WebQuilt visualization provides filtering capabilities and semantic zooming, allowing the designer to understand the test results at the gestalt view of the entire graph, and then drill down to sub-paths and single pages. The visualization highlights important usability issues, such as pages where users spent a lot of time, pages where users get off track during the task, navigation patterns, and exit pages, all within the context of a specific task. WebQuilt is designed to conduct remote usability testing on a variety of Internet-enabled devices and provide a way to identify potential usability problems when the tester cannot be present to observe and record user actions.

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.

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.

WebQuilt is a web logging and visualization system that helps web design teams run usability tests (both local and remote) and analyze the collected data. Logging is done through a proxy, overcoming many of the problems with server-side and client-side logging. Captured usage traces can be aggregated and visualized in a zooming interface that shows the web pages people viewed. The visualization also shows the most common paths taken through the web site for a given task, as well as the optimal path for that task, as designated by the designer. This paper discusses the architecture of WebQuilt and also describes how it can be extended for new kinds of analyses and visualizations.

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.