Clustering declustered data for efficient retrieval
Proceedings of the eighth international conference on Information and knowledge management
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As databases increasingly integrate 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. It becomes necessary to support the efficient retrieval of large amounts of multimedia data. Declustering techniques for multi-disk architectures have been effectively used for storage in relational databases. 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 work on 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 techniques based on hyperpyramid partitioning which reduces the number of buckets retrieved by the query and has very efficient inter and intra disk organizations. We evaluate the performance of proposed techniques by comparing it with the current best approaches. The new techniques lead to very significant improvement, up to 43 times, over the existing techniques, therefore resulting in fast retrieval of multimedia data.