Most similarity search techniques map the data objects into some high-dimensional feature space. The similarity search then corresponds to a nearest-neighbor seamh in the feature space which is computationally very intensive. In this paper, we present anew parallel method for fast nearest-neighbor search in high-dimensional feature spaces. The core problem of designing a parallel nearestneighbor algorithm is to find an adequate distribution of the data onto the disks. Unfortunately, the known dechrstering methods do not perform well for high-dimensional nearest-neighbor search. In contrast, our method has been optimized based on the special properties of high-dimensional spaces and therefore provides a near-optimal distribution of the data items among the disks. The basic idea , of our data declustering technique is to assign the buckets corresponding to different quadrants of the data space to different disks. We show that our technique -in contrast to other declustering methods -guarantees that all buckets corresponding to neighboring quadrants are assigned to different disks. We evaluate our method using large amounts of real data (up to 40 MBytes) and compare it with the best known data declustering method, the Hilbert curve. Our experiments show that our method provides an almost linear speed-up and a constant scale-up. Additionally, it outperforms the Hilbert approach by a factor of up to 5.
Another approach related to nearest neighbor query processing in high-dimensional space is the parallel method described in REF .
Fast parallel similarity search in multimedia databases
SIGMOD '97
Computer Science