In this paper we present a scalable and distributed access structure for similarity search in metric spaces. The approach is based on the Content-addressable Network (CAN) paradigm, which provides a Distributed Hash Table (DHT) abstraction over a Cartesian space. We have extended the CAN structure to support storage and retrieval of generic metric space objects. We use pivots for projecting objects of the metric space in an N-dimensional vector space, and exploit the CAN organization for distributing the objects among the computing nodes of the structure. We obtain a Peer-to-Peer network, called the MCAN, which is able to search metric space objects by means of the similarity range queries. Experiments conducted on our prototype system confirm full scalability of the approach. © Springer-Verlag Berlin Heidelberg 2007.
Falchi, F., Gennaro, C., & Zezula, P. (2007). A content-addressable network for similarity search in metric spaces. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4125 LNCS, pp. 98–110). Springer Verlag. https://doi.org/10.1007/978-3-540-71661-7_9