Dynamic Spatial Approximation Trees for Massive Data

Abstract

Metric space searching is an emerging technique to address the problem of efficient similarity searching in many applications, including multimedia databases and other repositories handling complex objects. Although promising, the metric space approach is still immature in several aspects that are well established in traditional databases. In particular, most indexing schemes are not dynamic, that is, few of them tolerate insertion of elements at reasonable cost over an existing index and only a few work efficiently in secondary memory. In this paper we introduce a secondary-memory variant of the Dynamic Spatial Approximation Tree, which has shown to be competitive in main memory. The resulting index handles well the secondary memory scenario and is competitive with the state of the art, becoming a useful alternative in a wide range of database applications. Moreover, our ideas are applicable to other secondary-memory trees where there is little control over the tree shape.