SETS: search enhanced by topic segmentation

Mayank Bawa, G. Manku, P. Raghavan
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引用次数: 199

Abstract

We present SETS, an architecture for efficient search in peer-to-peer networks, building upon ideas drawn from machine learning and social network theory. The key idea is to arrange participating sites in a topic-segmented overlay topology in which most connections are short-distance, connecting pairs of sites with similar content. Topically focused sets of sites are then joined together into a single network by long-distance links. Queries are matched and routed to only the topically closest regions. 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.
set:通过主题分割增强的搜索
我们提出了一种基于机器学习和社会网络理论的思想,在点对点网络中进行高效搜索的体系结构SETS。关键思想是将参与的站点安排在主题分段的覆盖拓扑中,其中大多数连接是短距离的,连接具有相似内容的站点对。然后,通过长距离链接将集中于特定主题的站点集合连接到一个单独的网络中。查询只匹配并路由到最接近主题的区域。我们讨论了set实现者可能面临的各种设计问题和权衡。我们证明了SETS在网络流量和查询处理负载方面是有效的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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