Efficient peer-to-peer similarity query processing for high-dimensional data

Objects, such as a digital image, a text document or a DNA sequence are usually represented in a high dimensional feature space. A fundamental issue in (peer-to-peer) P2P systems is to support an efficient similarity search for high-dimensional data in metric spaces. Prior works suffer from some fundamental limitations, such as being not adaptive to a highly dynamic network, poor search efficiency under skewed data scenarios, large maintenance overhead and etc. In this study, we propose an efficient scheme, Dragon, to support P2P similarity search in metric spaces. Dragon achieves the efficiency through the following designs: 1) Dragon is based on our previous designed P2P network, Phoenix, which has the optimal routing efficiency in dynamic scenarios. 2) We design a locality-preserving naming algorithm and a routing tree for each peer in Phoenix to support range queries. A radius-estimated method is proposed to transform a kNN query to a range query. 3) A load-balancing algorithm is given to support strong query processing under skewed data distributions. Extensive experiments verify the superiority of Dragon over existing works.