Making Peer-to-Peer Keyword Searching Feasible Using Multi-level Partitioning

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.

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