Data declustering speeds up large data set retrieval by partitioning the data across multiple disks or sites and performing retrievals in parallel. Performance is determined by how the data is broken into ”buckets” and how the buckets are assigned to disks. While some work has been done for declustering uniformly distributed low dimensional data, little work has been done on declustering non-un...

In this paper, we propose an eÆcient declustering algorithm which is adaptable in di erent data distribution. Previous declustering algorithms have a potential drawback by assuming data distribution is uniform. However, our method shows a good declustering performance for spatial data regardless of data distribution by taking it into consideration. First, we apply a spatial clustering algorithm...

In this study, parallel implementation of M-tree to index high dimensional metric space has been elaborated and an optimal declustering technique has been proposed. First, we have defined the optimal declustering and developed an algorithm based on this definition. Proposed declustering algorithm considers both object proximity and data load on disk/processors by executing a k-NN or a range que...

Multi-disk systems, coupled with declustering schemes, have been widely used in various applications to improve I/O performance by enabling parallel disk accesses. A declustering scheme determines how data blocks should be placed among multiple disks to maximize the parallelism. We focus on the problem of declustering grid-structured multidimensional data with the objective of reducing the resp...

We propose a method to achieve declustering for cartesian product les on M units. The focus is on range queries, as opposed to partial match queries that older declustering methods have examined. Our method uses a distance-preserving mapping, namely, the Hilbert curve, to impose a linear ordering on the multidimensional points (buckets); then, it traverses the buckets according to this ordering...

Data declustering is an important issue for reducing query response times in multi-disk database systems. In this paper, we propose a declustering method that utilizes the available information on query distribution, data distribution, data-item sizes, and disk capacity constraints. The proposed method exploits the natural correspondence between a data set with a given query distribution and a ...