Experienced users who query search engines have a complex behavior. They explore many topics in parallel, experiment with query variations, consult multiple search engines, and gather information over many sessions. I...
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Experienced users who query search engines have a complex behavior. They explore many topics in parallel, experiment with query variations, consult multiple search engines, and gather information over many sessions. In the process they need to keep track of search context - namely useful queries and promising result links, which can be hard. We present an extension to search engines called SearchPad that makes it possible to keep track of 'search context' explicitly. We describe an efficient implementation of this idea deployed on four search engines: AltaVista, Excite, Google and Hotbot. Our design of SearchPad has several desirable properties: (i) portability across all major platforms and browsers;(ii) instant start requiring no code download or special actions on the part of the user;(iii) no server side storage;and (iv) no added client-server communication overhead. An added benefit is that it allows search services to collect valuable relevance information about the results shown to the user. In the context of each query SearchPad can log the actions taken by the user, and in particular record the links that were considered relevant by the user in the context of the query. The service was tested in a multi-platform environment with over 150 users for 4 months and found to be usable and helpful. We discovered that the ability to maintain search context explicitly seems to affect the way people search. Repeat SearchPad users looked at more search results than is typical on the Web, suggesting that availability of search context may partially compensate for non-relevant pages in the ranking. (C) 2000 Published by Elsevier Science B.V. All rights reserved.
This paper builds on the results from the Viseum project where we built an image server/client system to allow browsing of very large images. In the follow-on European ACOHIR project we built systems capable of acquir...
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This paper builds on the results from the Viseum project where we built an image server/client system to allow browsing of very large images. In the follow-on European ACOHIR project we built systems capable of acquiring colour-calibrated high-resolution views of objects from many positions. A Java viewer allows the user to closely examine objects in a similar way to Quicktime VR but with much higher resolution. The Intemet Imaging Protocol is used to allow the viewer to request 64 x 64 pel tiles on demand to allow fast browsing of the objects in a Web browser. The original image data occupy typically around 200 MBytes yet we can provide almost instantaneous views with zooming and acceptable performance across the Internet or a modem. The approach taken in the Java viewer is modular and easily customised using javascript. Caching at both the server and client provide improved performance. This paper shows how the techniques developed for large images have been applied and modified to handle high-resolution object views. (C) 2000 Published by Elsevier Science B.V. All rights reserved.
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