Making peer-to-peer keyword searching feasible using multi-level partitioning

Authors:
Shuming Shi;Guangwen Yang;Dingxing Wang;Jin Yu;Shaogang Qu;Ming Chen
Affiliations:
Department of Computer Science and Technology, Tsinghua University, Beijing, P.R. China;Department of Computer Science and Technology, Tsinghua University, Beijing, P.R. China;Department of Computer Science and Technology, Tsinghua University, Beijing, P.R. China;Department of Computer Science and Technology, Tsinghua University, Beijing, P.R. China;Department of Computer Science and Technology, Tsinghua University, Beijing, P.R. China;Department of Computer Science and Technology, Tsinghua University, Beijing, P.R. China
Venue:
IPTPS'04 Proceedings of the Third international conference on Peer-to-Peer Systems
Year:
2004

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Abstract

This paper discusses large scale keyword searching on top of peer-to-peer (P2P) networks. The state-of-the-art keyword searching techniques for unstructured and structured P2P systems are query flooding and inverted list intersection respectively. However, it has been demonstrated that P2P-based large scale full-text searching is not feasible by using either of the two techniques. We propose in this paper a new index partitioning and building scheme, multi-level partitioning (MLP), and discuss its implementation on top of P2P networks. MLP can dramatically reduce bisection bandwidth consumption and end-user latency compared with the partition-by-keyword scheme. And comparing with partition-by-document, it need only broadcast a query to moderate number of peers to generate precise results.