Abstract

Similarity search is one of the typical query type for multimedia retrieval, data mining and decision support systems. Many similarity measures transform objects into points in a high-dimensional vector space and define similarity of two objects with respect to their distance in the vector space. Data-partitioning index methods for such spaces like R-tree or X-tree are known to deteriorate with the increased number of dimensions. A newer approach, the so called VA-File, overcomes the difficulties of high dimensionality. However, the VA-File search method is linear in the number of objects. In this paper, we investigate parallelization issues of that linear process either by using multiple disks assigned to a single processor, or by using a cluster of workstation. Performance measurements of parallel VA-File in a multiple disk environment are very promising and show almost linear speed-up with the increased number of disks. Distributed VA-File search in a cluster of workstation not only...

Citations