Ali Şaman Tosun
Abstract
Declustering distributes data among parallel disks to reduce retrieval cost using I/O parallelism. Many schemes were proposed for single copy declustering of spatial data. Recently, declustering using replication gained a lot of interest and several schemes with different properties were proposed. It is computationally expensive to verify optimality of replication schemes designed for range queries and existing schemes verify optimality for up to 50 disks. In this article, we propose a novel method to find replicated declustering schemes that render all spatial range queries optimal. The proposed scheme uses threshold based declustering, divisibility of large queries for optimization and optimistic approach to compute maximum flow. The proposed scheme is generic and works for any number of dimensions. Experimental results show that using 3 copies there exist allocations that render all spatial range queries optimal for up to 750 disks in 2 dimensions and with the exception of several values for up to 100 disks in 3 dimensions. The proposed scheme improves search for strictly optimal replicated declustering schemes significantly and will be a valuable tool to answer open problems on replicated declustering.
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Index Terms
- Divide-and-conquer scheme for strictly optimal retrieval of range queries
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Reviews
David Gary Hill
The most common query type in a large-say a terabyte (TB) or larger-database, such as a relational or spatial database, is a range query. In a range query, a user specifies a range of values for each dimension of interest within a dataset. The user receives an output result of "the set of items in the dataset that have values within the specified range for each dimension." When the efficient retrieval of all the requested items is a challenge, the time it takes to finish retrieving all of the items from a range query might be unacceptably long. Previous research has focused on efficient retrieval structures and methods that use input/output (I/O) parallelism, which involves storage techniques that access data from multiple disks. Numerous declustering schemes-that distribute data among parallel disks-have been proposed. This paper discusses overcoming the limitations of single-copy declustering schemes, and proposes using replication to achieve optimal queries. Tosun claims that the proposed replicated declustering scheme is "generic and works for any number of dimensions." The paper covers in mathematical detail the proposed scheme. Tosun includes a couple of examples that show how this approach might be integrated with commercial applications. Readers who have a technical interest in speeding up range queries for databases may find this paper useful. Online Computing Reviews Service
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