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.
Batko, M., Novak, D., Falchi, F., & Zezula, P. (2006). On scalability of the similarity search in the world of peers. In Proceedings of the 1st international conference on Scalable information systems InfoScale 06 (pp. 20-es). ACM Press. https://doi.org/10.1145/1146847.1146867