Fast Parallel Similarity Search in Multimedia Databases

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Fast Parallel Similarity Search in Multimedia Databases
Most similarity search techniques map the data objects into some high-dimensional feature space. The similarity search then corresponds to a nearest-neighbor search in the feature space which is computationally very intensive. In this paper, we present a new 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 declustering 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 - g...
Stefan Berchtold, Christian Böhm, Bernhard Br
Added 07 Aug 2010
Updated 07 Aug 2010
Type Conference
Year 1997
Authors Stefan Berchtold, Christian Böhm, Bernhard Braunmüller, Daniel A. Keim, Hans-Peter Kriegel
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