Efficient Range Query Processing in Metric Spaces Over Highly Distributed Data
Similarity search in P2P systems has attracted a lot of attention recently and several important applications, like distributed image search, can profit from the proposed distributed algorithms. In this paper, the authors address the challenging problem of efficient processing of range queries in metric spaces, where data is horizontally distributed across a super-peer network. Their approach relies on SIMPEER, a framework that dynamically clusters peer data, in order to build distributed routing information at super-peer level. SIMPEER allows the evaluation of exact range and nearest neighbor queries in a distributed manner that reduces communication cost, network latency, bandwidth consumption and computational overhead at each individual peer.