Similarity searching: Towards bulk-loading peer-to-peer networks

Due to the exponential growth of digital data and its complexity, we need a technique which allows us to search such collections efficiently. A suitable solution seems to be based on the peer-to-peer (P2P) network paradigm and the metric-space model of similarity. During the building phase of the distributed structure, the peers often split as new peers join the network. During a peer split, the local data is halved and one half is migrated to the new peer. In this paper, we study the problem of efficient splits of metric data locally organized by an M-tree and we propose a novel algorithm for speeding the splits up. In particular, we focus on the metric-based structured P2P network called the M-Chord. In experimental evaluation, we compare the proposed algorithm with several straightforward solutions on a real network organizing 10 million images. Our algorithm provides a significant performance boost.