Abstract

This Ph.D. thesis concerns the problem of distributed indexing techniques for similarity search in metric spaces. Solutions for efficient evaluation of similarity queries, such as range or nearest neighbor queries, existed only for centralized systems. However, the amount of data produced in digital form grows exponentially every year and the traditional paradigm of one huge database system holding all the data seems to be insufficient. The distributed computing paradigm - especially the peer-to-peer data networks and GRID infrastructure - is a promising solution to the problem, since it allows to employ virtually unlimited pool of computational and storage resources. Nevertheless, the centralized indexing similarity searching structures cannot be directly used in the distributed environment and some adjustments and design modifications are needed. In this thesis, we describe a distributed metric space based index structure, which was, as far as we know, the very first distributed solution in this area. It adopts the peer-to-peer data network paradigm and implements the basic two similarity queries - the range query and the k-nearest neighbors query. The technique is fully scalable and can grow easily over practically unlimited number of computers. It is also strictly decentralized, there is no "global" centralized component, thus the emergence of hot spots is minimized. The properties of the structure are verified experimentally and we also provide a comprehensive comparison of this method with another three distributed metric space indexing techniques that were proposed so far. …víceméně