On the selectivity of multidimensional routing indices

Recently, the problem of efficiently supporting advanced query operators, such as nearest neighbor or range queries, over multidimensional data in widely distributed environments has attracted much attention. In unstructured peer-to-peer (P2P) networks, peers store data in an autonomous manner, thus multidimensional routing indices (MRI) are required, in order to route user queries efficiently to only those peers that may contribute to the query result set. Focusing on a hybrid unstructured P2P network, in this paper, we analyze the parameters for building MRI of high selectivity. In the case where similar data are located at different parts of the network, MRI exhibit extremely poor performance, which renders them ineffective. We present algorithms that boost the query routing performance by detecting similar peers and reassigning these peers to other parts of the hybrid network in a distributed and scalable way. The resulting MRI are able to eagerly discard routing paths during query processing. We demonstrate the advantages of our approach experimentally and show that our framework enhances a state-of-the-art approach for similarity search in terms of reduced network traffic and number of contacted peers.