A dynamic routing protocol for keyword search in unstructured peer-to-peer networks

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.