Search and replication in unstructured peer-to-peer networks
ICS '02 Proceedings of the 16th international conference on Supercomputing
Machine Learning
Mapping the Gnutella Network: Macroscopic Properties of Large-Scale Peer-to-Peer Systems
IPTPS '01 Revised Papers from the First International Workshop on Peer-to-Peer Systems
Routing Indices For Peer-to-Peer Systems
ICDCS '02 Proceedings of the 22 nd International Conference on Distributed Computing Systems (ICDCS'02)
Improving Search in Peer-to-Peer Networks
ICDCS '02 Proceedings of the 22 nd International Conference on Distributed Computing Systems (ICDCS'02)
Making gnutella-like P2P systems scalable
Proceedings of the 2003 conference on Applications, technologies, architectures, and protocols for computer communications
On the Origin of Power Laws in Internet Topologies
On the Origin of Power Laws in Internet Topologies
Adaptive Probabilistic Search for Peer-to-Peer Networks
P2P '03 Proceedings of the 3rd International Conference on Peer-to-Peer Computing
A Distributed Approach to Solving Overlay Mismatching Problem
ICDCS '04 Proceedings of the 24th International Conference on Distributed Computing Systems (ICDCS'04)
Searching the peer-to-peer networks: the community and their queries
Journal of the American Society for Information Science and Technology - Special issue: Part II: Information seeking research
Reinforcement Learning for Query-Oriented Routing Indices in Unstructured Peer-to-Peer Networks
P2P '06 Proceedings of the Sixth IEEE International Conference on Peer-to-Peer Computing
A statistical study of today’s gnutella
APWeb'06 Proceedings of the 8th Asia-Pacific Web conference on Frontiers of WWW Research and Development
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The idea of building query-oriented routing indices has changed the way of improving keyword search efficiency from the basis as it can learn the content distribution from the query routing process. It gradually improves search efficiency without excessive network overhead for the construction and maintenance of routing indices. However, previously proposed protocol is not practically effective due to the slow improvement of routing efficiency. In this paper, we propose a novel protocol for query-oriented routing indices which quickly achieves high search efficiency at low cost. The maintenance mechanism employs reinforcement learning to exploit mass peer behavior. It explicitly uses the expected number of returned results to depict the content distribution, which helps quickly approximate the real distribution. The routing mechanism is to retrieve as many contents as possible and help speed up the learning process. To further improve the search efficiency, several methods are taken to optimize the routing and maintenance mechanism. In dealing with multi-keyword queries, the information of corresponding keywords is also used to forward the queries. In addition, to accelerate the learning speed, a rough description of content distribution is achieved when the query is first seen. The experimental evaluation shows that the mechanism achieves high routing efficiency, quick learning ability, and satisfactory performance under churn.