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SETS: Search Enhanced by Topic-Segmentation

Bawa, Mayank and Manku, Gurmeet and Raghavan, Prabhakar (2003) SETS: Search Enhanced by Topic-Segmentation. Technical Report. Stanford InfoLab.




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.

Item Type:Techreport (Technical Report)
Subjects:Computer Science > Distributed Systems
Related URLs:Project Homepage
ID Code:632
Deposited By:Import Account
Deposited On:31 Mar 2004 16:00
Last Modified:24 Dec 2008 08:51

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