Adaptive Approximate Similarity Searching through Metric Social Networks

Abstract

Exploiting the concepts of social networking represents a novel approach to the approximate similarity query processing. We present a metric social network where relations between peers, giving similar results, are established on per-query basis. Based on the universal law of generalization, a new query forwarding algorithm is proposed. The same principle is used to manage query histories of individual peers with the possibility to tune the tradeoff between the extent of the history and the level of the query-answer approximation. All algorithms are tested on real data and real network of computers.