On scalability of the similarity search in the world of peers

Due to the increasing complexity of current digital data, similarity search has become a fundamental computational task in many applications. Unfortunately, its costs are still high and the linear scalability of single server implemen-tations prevents from efficient searching in large data vol-umes. In this paper, we shortly describe four recent scalable distributed similarity search techniques and study their per-formance of executing queries on three different datasets. Though all the methods employ parallelism to speed up query execution, different advantages for different objec-tives have been identified by experiments. The reported re-sults can be exploited for choosing the best implementations for specific applications. They can also be used for design-ing new and better indexing structures in the future. 1