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Shneiderman, B.; Byrd, D.; Croft, W.B.: Clarifying search : a user-interface framework for text searches (1997)
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- Abstract
- Current user interfaces for textual database searching leave much to be desired: individually, they are often confusing, and as a group, they are seriously inconsistent. We propose a four- phase framework for user-interface design: the framework provides common structure and terminology for searching while preserving the distinct features of individual collections and search mechanisms. Users will benefit from faster learning, increased comprehension, and better control, leading to more effective searches and higher satisfaction.
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Sieverts, E.: Liever browsen dan zoeken (1998)
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- Abstract
- Despite development of the WWW searchers still experience difficulties following links between sites and cannot be sure that a site contains the required information. 3 software programs developed to guide users through the maze of hyperlinks are: Dynamic diagrams, the Hyperbolic tree, and the Brain. in contrast to the other programs which operate on webservers and display hyperlinks in diagrammatic form the Brain is installed on individual PCs and can be customised to meet users' requirements
- Footnote
- Übers. d. Titels: A preference for browsing rather than searching
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Zamir, O.; Etzioni, O.: Grouper : a dynamic clustering interface to Web search results (1999)
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- Abstract
- Clustering is an effective way of organizing documents into collections for ease of browsing. Recently with the growth of WWW, clustering has become a paradigm for organizing search results. Online systems face many new challenges, including the need for fast response time, generating high quality clusters with simple descriptions for novice users, and working with document distributions that violates many traditional assumptions. How do different clustering algorithms trade off quality of clusters and speed? What modifications are necessary to adapt traditional clustering algorithm to the WWW? How do these system scale to larger document collection? How do these systems evaluate the quality of the cluster they generate? How are the clusters generated in each case, and are there any processing after cluster generation to improve on the cluster quality?
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