Institute for Systems Research Technical Reports

Permanent URI for this collectionhttp://hdl.handle.net/1903/4376

This archive contains a collection of reports generated by the faculty and students of the Institute for Systems Research (ISR), a permanent, interdisciplinary research unit in the A. James Clark School of Engineering at the University of Maryland. ISR-based projects are conducted through partnerships with industry and government, bringing together faculty and students from multiple academic departments and colleges across the university.

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Author: search.filters.author.Shneiderman, BenSubject: search.filters.subject.information visualization

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Now showing 1 - 4 of 4
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    Knowledge Discovery in High Dimensional Data: Case Studies and a User Survey for an Information Visualization Tool
    (2005) Seo, Jinwook; Shneiderman, Ben; ISR
    Knowledge discovery in high dimensional data is a challenging enterprise, but new visual analytic tools appear to offer users remarkable powers if they are ready to learn new concepts and interfaces. Our 3-year effort to develop versions of the Hierarchical Clustering Explorer (HCE) began with building an interactive tool for exploring clustering results. It expanded, based on user needs, to include other potent analytic and visualization tools for multivariate data, especially the rank-by-feature framework. Our own successes using HCE provided some testimonial evidence of its utility, but we felt it necessary to get beyond our subjective impressions. This paper presents an evaluation of the Hierarchical Clustering Explorer (HCE) using three case studies and an email user survey (n=57) to focus on skill acquisition with the novel concepts and interface for the rank-by-feature framework. Knowledgeable and motivated users in diverse fields provided multiple perspectives that refined our understanding of strengths and weaknesses. A user survey confirmed the benefits of HCE, but gave less guidance about improvements. Both evaluations suggested improved training methods.
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    Domain Name Based Visualization of Web Histories in a Zoomable User Interface
    (2000) Gandhi, Rajiv; Kumar, Girish; Bederson, Benjamin B.; Shneiderman, Ben; ISR
    Users of hypertext systems like the World Wide Web (WWW) often find themselves following hypertext links deeper and deeper, only to become "lost" and unable to find their way back to the previouslyvisited pages. We have implemented a web browser companion called Domain Tree Browser (DTB) that builds a tree structured visual navigation history while browsing the web. The Domain Tree Browser organizes the URLs visited based on the domain name of each URL and shows thumbnails of each page in a zoomable window. A usability test was conducted with four subjects.
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    Data Object and Label Placement for Information Abundant Visualizations
    (1998) Li, Jia; Plaisant, Catherine; Shneiderman, Ben; ISR
    Placing numerous data objects and their corresponding labels in limited screen space is a challenging problem in information visualization systems. Extending map-oriented techniques, this paper describes static placement algorithms and develops metrics (such as compactness and labeling rate) as a basis for comparison among these algorithms. A control panel facilitates user customization by showing the metrics for alternative algorithms. Dynamic placement techniques that go beyond map-oriented techniques demonstrate additional possibilities. User actions can lead to selective display of data objects and their labels.
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    Design and Evaluation of Incremental Data Structures and Algorithms for Dynamic Query Interfaces
    (1997) Tanin, Egemen; Beigel, Richard; Shneiderman, Ben; ISR
    Dynamic query interfaces (DQI) are a recently developed database access mechanism that provides continuous real-time feedback to the user during query formulation. Previous work shows that DQI are an elegant and powerful interface to small databases. Unfortunately, when applied to large databases, previous DQI algorithms slow to a crawl. We present a new incremental approach to DQI algorithms that works well with large databases, both in theory and in practice.