NDLI: Persistent clustered main memory index for accelerating k-NN queries on high dimensional datasets

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Persistent clustered main memory index for accelerating k-NN queries on high dimensional datasets

Persistent clustered main memory index for accelerating k-NN queries on high dimensional datasets

Content Provider	ACM Digital Library
Author	Thomasian, Alexander Zhang, Lijuan
Abstract	Similarity search implemented via k-Nearest-Neighbor (k-NN) queries is an extremely useful paradigm in content based image retrieval (CBIR), which is costly on high-dimensional indices due to the curse of dimensionality. We improve k-NN query processing by utilizing the double filtering effect of clustering and indexing on a persistent version of the Ordered-Partition tree (OP-tree) index, which is highly efficient in processing k-NN queries. The OP-tree is made persistent by writing it onto disk after serialization, i.e. arranging its nodes into contiguous memory locations, so that the high transfer rate of modern disk drives is exploited. We first report experimental results to optimize OP-tree parameters. We then compare OP-trees and sequential scans with options for the Karhunen-Loève transform and Euclidean distance calculation. Comparisons against OMNI-based sequential scan are also reported. We finally compare a clustered and persistent version of the OP-tree against a clustered version of the SR-tree and the VA-File method. It is observed that the OP-tree index outperforms the other two methods and that the improvement increases with the number of dimensions.
Starting Page	65
Ending Page	70
Page Count	6
File Format	PDF
ISBN	1595931511
DOI	10.1145/1160939.1160955
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2005-06-17
Publisher Place	New York
Access Restriction	Subscribed
Content Type	Text
Resource Type	Article

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