People often use a variety of computing devices, such as PCs, PDAs, and cell phones, to access the same information. The user interface to this information needs to be different for each device, due to the different input and output constraints of each device. Currently designers designing such multi-device user interfaces either have to design a UI separately for each device, which is time consuming, or use a program to automatically generate interfaces, which often result in interfaces that are awkward. Each method also discourages iterative design, considered critical for creating good user interfaces. We are creating a system called Damask to support the early-stage design of user interfaces targeted at multiple devices. With Damask, the designer will design a user interface for one device, by sketching the design and by specifying which design patterns the interface uses. The patterns will help Damask generate user interfaces optimized for the other devices targeted by the designer. The generated interfaces will be of sufficient quality so that it will be more convenient to use Damask than to design each of the other interfaces separately, and the ease with which designers will be able to create designs will encourage them to engage in iterative design. Damask will also

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, hypedink 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.

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. Keywords: Clustering, Log Analysis, Web Mining, User Profile, User Sessions, World Wide Web 1

Web browsing is inefficient for blind web users because of persistent accessibility problems, but the extent of these problems and their practical effects from the perspective of the user has not been sufficiently examined. We conducted a study in situ to investigate the accessibility of the web as experienced by web users. This remote study used an advanced web proxy that leverages AJAX technology to record both the pages viewed and the actions taken by users on the web pages that they visited. Our study was conducted remotely over the period of one week, and our participants used the assistive technology and software to which they were already accustomed and had already configured according to preference. These advantages allowed us to aggregate observations of many users and to explore the practical effects on and coping strategies employed by our blind participants. Our study reflects web accessibility from the perspective of web users and describes quantitative differences in the browsing behavior of blind and sighted web users. Categories and Subject Descriptors K.4.2 [Social Issues]: Assistive technologies for persons

Interface designs are currently tested in a mixture of fidelities and media. So far, there is insufficient research to indicate what level of fidelity and media will produce the best feedback from users. This experiment compared user testing with low- and high-fidelity prototypes in both computer and paper media. Task-based user tests of sketched (low-fidelity) and HTML (high-fidelity) website prototypes were conducted in each medium, separating the testing medium from other factors of prototype fidelity. We found that low- and high-fidelity prototypes are equally good at uncovering usability issues. Usability testing results were also found to be independent of medium, despite differences in interaction style. Designers should choose whichever medium and level of fidelity suit their practical needs and design goals, as discussed in this paper.

Software process discovery has historically been an intensive task, either done through exhaustive empirical studies or in an automated fashion using techniques such as logging and analysis of command shell operations. While empirical studies have been fruitful, data collection has proven to be tedious and time consuming. Existing automated approaches have expedited collection of fine-grained data, but do so at the cost of impinging on the developer's work environment, few of who may be observed. In this paper, we explore techniques for discovering development processes from publicly available open source software development repositories that exploit advances in artificial intelligence. Our goal is to facilitate process discovery in ways that are less cumbersome than empirical techniques and offer a more holistic, task-oriented view of the process than current automated systems provide.

Abstract not found

This paper describes the concepts of TEA, a flexible tool that supports user tests by automating repetitive tasks and collecting data of user inputs and actions. TEA was specifically designed for...

Abstract—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. Index Terms—Geographical visualization, GIS, heatmap, server log analysis, online mapping systems, social navigation. 1

Process discovery has been shown to be challenging offering limited results, however, most work has been conducted in closed source systems. This paper describes a new approach to process discovery that examines the Internet information spaces of open source software development projects. In searching for an automated solution to the process discovery problem, we first have simulated it by having a human act as an “intelligent spider” searching the Web space for evidence of process activities and reconstructing process fragments based on the clues discovered. The purpose of such an approach is to help reveal the details of our manual process discovery approach. In turn, such knowledge can then be employed to determine the requirements and design of automated process discovery and modeling mechanisms that can be applied to Web-based software development projects.