| Available via | http://dbpubs.stanford.edu/pub/2003-80 |
|
Submitted on |
1st of April 2004 |
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Author |
Bawa, Mayank; Manku, Gurmeet; Raghavan, Prabhakar |
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Title |
SETS: Search Enhanced by Topic-Segmentation |
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Date of publication |
July 2003 |
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Published in |
ACM SIGIR, 2003 |
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Number of pages |
8 |
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Language |
English |
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Project |
Peers |
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Type |
Other |
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Subject group |
Distributed Systems |
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Abstract |
We present SETS, an architecture for building topic-segmented networks for efficient search. The key idea is to arrange participants in a topic-segmented topology where most of the links are short-distance links joining pairs of sites with similar content. The resulting topically focused regions are joined together into a single network by long-distance links. Queries are then matched and routed to only the topically closest regions. We draw on ideas from machine learning and social network theory to build an efficient search network. We discuss a variety of design issues and tradeoffs that an implementor of SETS would face. We show that SETS is efficient in network traffic and query processing load. |
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| Management of the document by | siroker@db.stanford.edu
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