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Due to the inability of traditional keyword-based retrieval using semantic meaning in Peer-to-Peer networks, large bandwidth is wasted; meanwhile desirable information is still far beyond the search results most algorithms could gain. How to locate the needed peer accurately and how to obtain requested information
In this paper we focus on building keyword search service over unstructured Peer-to-Peer (P2P) networks. Current state-of-the-art keyword search approaches for unstructured P2P systems are either blind or informed. Blind search methods such as flooding in Gnutella generate a large of redundant cloned messages and
between the DHT overlay and the semantic overlay to support the search of a keyword sequence. Its time cost is sublinear with the length of the keyword sequence. Analysis and experiments show that the DST-based search is fast, load-balanced, and useful in realizing an accurate content search on P2P networks.
identification based BP Neural Network, and without relying on keyword matching. This article introduces BP algorithm, analyzes the characters of P2P traffic, gives out the BP network based on connection patterns ofP2P networks. The trained BPNN was applied as a P2P traffic identifier, which can be used to distinguish any kind of
, extraction time to seconds, and search duration into milliseconds. Furthermore, the uniform distribution of the extracted fingerprints enables the usage of existing DHT-based keyword search methods for fingerprint queries.
Searching with partial knowledge and misspelled keywords is a challenging problem in peer-to-peer networks. This paper presents FM-chord, a novel searching mechanism for information retrieval with queries containing spelling and pronunciation mistakes in keywords using a widely-used structured overlay Chord. FM-chord
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