| dc.description.abstract (摘要) | On query processing in a large database, similar top-k documents query is an important mechanism to retrieve the highly correlated document collection with query for users. It ranks documents with a similarity ranking function and reports the k documents with highest similarity. However, the former approach in web searching, i.e., centralized search engines, rises some issues such as lack of coverage and scalability, impact provides rank-based query become a costly operation. Recently, using Peer-to-peer (P2P) architectures to tackle above issues has emerged as a trend of solution, but due to the shortage of global knowledge and some appropriate central coordinators, support rank-based query in highly distributed environment has been difficulty.In this paper, we proposed a framework to solve these problems. First, we performed the local cluster pre-processing on each peer, followed by the zone creation process, forming sub-zones over P2P network, and then constructing the feature index table to improve the performance of selecting similar top-k cluster results. The experiments show that our approach performs similar top-k documents query outperforms than SON-based approach in highly distributed environment. | en_US |
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