Asymptotically Optimal Declustering Schemes for Range Queries

Sinha, Rakesh Kumar; Bhatia, Randeep; Chen, Chung‐Min

doi:10.1007/3-540-44503-x_10

Cited by 19 publications

(28 citation statements)

References 16 publications

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“…it is the maximum number of tiles in Q retrieved from a single disk). These schemes include Disk Modulo DM [6], Fieldwise eXclusive (FX) or [9], the cyclic schemes (including RPHM, GFIB, and EXH) [11], GRS [4], a technique developed by Atallah and Prabhakar [2] which we will call RFX, and several techniques based on discrepancy theory [5,14] (for an introduction to discrepancy theory see [10]). Note that these are just a subset of the declustering techniques that have been developed for this problem.…”

Section: Related Workmentioning

confidence: 99%

“…A scheme based on the Corput set is defined in [14] that is similar to GRS except that the k i values are…”

Section: Related Workmentioning

confidence: 99%

“…A declustering is called strictly optimal if all range queries can be answered optimally, however it was shown in [1] that this is not achievable except in a few limited circumstances. Thus there has been a significant amount of work to develop declustering schemes that have close to optimal performance, a sampling of which are in [2,3,4,5,6,9,11,14].…”

Section: Introductionmentioning

confidence: 99%

“…In this paper we focus on answering range queries in a multidimensional database (such as a GIS), where each of its dimensions is divided uniformly to obtain tiles which are placed on different disks; there has been a significant amount of research for this problem (a subset of which is [1,2,3,4,5,6,7,8,9,11,12,13,14,15]). A declustering scheme would be optimal if any range query could be answered by doing no more than # of tiles inside the range/# of disks retrievals from any one disk.…”

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confidence: 99%

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Replicated Parallel I/O without Additional Scheduling Costs

Atallah

Frikken

2003

Lecture Notes in Computer Science

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Abstract.A common technique for improving performance in a database is to decluster the database among multiple disks so that data retrieval can be parallelized. In this paper we focus on answering range queries in a multidimensional database (such as a GIS), where each of its dimensions is divided uniformly to obtain tiles which are placed on different disks; there has been a significant amount of research for this problem (a subset of which is [1,2,3,4,5,6,7,8,9,11,12,13,14,15]). A declustering scheme would be optimal if any range query could be answered by doing no more than # of tiles inside the range/# of disks retrievals from any one disk. However, it was shown in [1] that this is not achievable in many cases even for two dimensions, and therefore much of the research in this area has focused on developing schemes that performed close to optimal. Recently, the idea of using replication (i.e. placing records on more than one disk) to increase performance has been introduced. [7,12,13,15]. If replication is used, a retrieval schedule (i.e. which disk to retrieve each tile from) must be computed whenever a query is being processed. In this paper we introduce a class of replicated schemes where the retrieval schedule can be computed in time O(# of tiles inside the query's range), which is asymptotically equivalent to query retrieval for the non-replicated case. Furthermore, this class of schemes has a strong performance advantage over non-replicated schemes, and several schemes are introduced that are either optimal or are optimal plus a constant additive factor. Also presented in this paper is a strictly optimal scheme for any number of colors that requires the lowest known level of replication of any such scheme.

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Section: Related Workmentioning

confidence: 99%

“…A scheme based on the Corput set is defined in [14] that is similar to GRS except that the k i values are…”

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Replicated Parallel I/O without Additional Scheduling Costs

Atallah

Frikken

2003

Lecture Notes in Computer Science

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“…Given the established bounds on the extra cost and the impossibility result, a large number of declustering techniques have been proposed to achieve performance close to the bounds either on the average case [5], [12], [13], [14], [16], [22], [24], [25], [29], [31], [36], [37] or, in the worst case, [3], [6], [7], [9], [41]. Although initial approaches in the literature were originally for relational databases or Cartesian product files, recent techniques focus more on spatial data declustering.…”

Section: Introductionmentioning

confidence: 99%

Analysis and Comparison of Replicated Declustering Schemes

Tosun

2007

IEEE Trans. Parallel Distrib. Syst.

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Abstract-Declustering distributes data among parallel disks to reduce the retrieval cost using I/O parallelism. Many schemes were proposed for the single-copy declustering of spatial data. Recently, declustering using replication gained a lot of interest and several schemes with different properties were proposed. An in-depth comparison of major schemes is necessary to understand replicated declustering better. In this paper, we analyze the proposed schemes, tune some of the parameters, and compare them for different query types and under different loads. We propose a three-step retrieval algorithm for the compared schemes. For arbitrary queries, the dependent and partitioned allocation schemes perform poorly; others perform close to each other. For range queries, they perform similarly with the exception of smaller queries in which random duplicate allocation (RDA) performs poorly and dependent allocation performs well. For connected queries, partitioned allocation performs poorly and dependent allocation performs well under a light load.

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