NDLI: Clustering declustered data for efficient retrieval

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Clustering declustered data for efficient retrieval

Content Provider	ACM Digital Library
Author	Ferhatosmanoglu, Hakan Agrawal, Divyakant El Abbadi, Amr
Abstract	Modern databases increasingly integrate new kinds of information, such as multimedia information in the form of image, video, and audio data. Both the dimensionality and the amount of data that need to be processed is increasing rapidly, increasing the demand for the efficient retrieval of large amounts of multi-dimensional data. Declustering techniques for multi-disk architectures have been effectively used for storage. In this paper, we first establish that besides exploiting the parallelism, a careful organization of each disk must be considered for fast searching. We introduce the notion of page allocation and data space mapping which can be used to organize and retrieve multidimensional data. We develop these notions based on three different partitioning strategies: regular grid partitioning, concentric hypercubes and hyperpyramids. We develop techniques that satisfy efficient retrieval by optimizing the number of buckets retrieved by the query, disk arm movement and I/O parallelism. We prove that concentric hypercube-based mapping satisfies the optimal clustering and optimal parallelism. We develop a technique based on hyperpyramid partitioning that reduces the number of buckets retrieved by the query and has efficient inter- and intra-disk organizations. We evaluate the performance of proposed techniques by comparing them with the current approaches. The new techniques lead to very significant improvement over the existing techniques, and result in fast retrieval of multi-dimensional data.
Starting Page	343
Ending Page	350
Page Count	8
File Format	PDF
ISBN	1581131461
DOI	10.1145/319950.320026
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	1999-11-01
Publisher Place	New York
Access Restriction	Subscribed
Content Type	Text
Resource Type	Article

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