Similarity search without tears: the OMNI-family of all-purpose access methods

Abstract

Designing a new access method inside a commercial DBMS is cumbersome and expensive. We propose a family of metric access methods that are fast and easy to implement on top of existing access methods, such as sequential scan, R-trees and Slim-trees. The idea is to elect a set of objects as foci, and gauge all other objects with their distances from this set. We show how to define the foci set cardinality, how to choose appropriate foci, and how to perform range and nearest-neighbor queries using them, without false dismissals. The foci increase the pruning of distance calculations during the query processing. Furthermore we index the distances from each object to the foci to reduce even triangular inequality comparisons. Experiments on real and synthetic datasets show that our methods match or outperform existing methods. They are up to 10 times faster, and perform up to 10 times fewer distance calculations and disk accesses. In addition, it scales up well, exhibiting sub-linear performance with growing database size.