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dc.contributor.authorKoyutürk, M.en_US
dc.contributor.authorAykanat, C.en_US
dc.date.accessioned2016-02-08T10:24:04Z
dc.date.available2016-02-08T10:24:04Z
dc.date.issued2005en_US
dc.identifier.issn3064379en_US
dc.identifier.urihttp://hdl.handle.net/11693/24097
dc.description.abstractData 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 hypergraph. We define an objective function that exactly represents the aggregate parallel query-response time for the declustering problem and adapt the iterative-improvement-based heuristics successfully used in hypergraph partitioning to this objective function. We propose a two-phase algorithm that first obtains an initial K-way declustering by recursively bipartitioning the data set, then applies multi-way refinement on this declustering. We provide effective gain models and efficient implementation schemes for both phases. The experimental results on a wide range of realistic data sets show that the proposed method provides a significant performance improvement compared with the state-of-the-art declustering strategy based on similarity-graph partitioning. © 2003 Elsevier Ltd. All rights reserved.en_US
dc.language.isoEnglishen_US
dc.source.titleInformation Systemsen_US
dc.relation.isversionofhttp://dx.doi.org/10.1016/j.is.2003.08.003en_US
dc.subjectDeclusteringen_US
dc.subjectHypergraph partitioningen_US
dc.subjectIterative improvementen_US
dc.subjectMax-cut graph partitioningen_US
dc.subjectParallel database systemsen_US
dc.subjectWeighted similarity graphen_US
dc.subjectAlgorithmsen_US
dc.subjectApproximation theoryen_US
dc.subjectConstraint theoryen_US
dc.subjectGraph theoryen_US
dc.subjectHeuristic methodsen_US
dc.subjectIterative methodsen_US
dc.subjectMathematical modelsen_US
dc.subjectParallel processing systemsen_US
dc.subjectDeclusteringen_US
dc.subjectHypergraph partitioningen_US
dc.subjectIterative improvementen_US
dc.subjectMax cut graph partitioningen_US
dc.subjectParallel database systemsen_US
dc.subjectWeighted similarity graphen_US
dc.subjectDatabase systemsen_US
dc.titleIterative-improvement-based declustering heuristics for multi-disk databasesen_US
dc.typeArticleen_US
dc.departmentDepartment of Computer Engineering
dc.citation.spage47en_US
dc.citation.epage70en_US
dc.citation.volumeNumber30en_US
dc.citation.issueNumber1en_US
dc.identifier.doi10.1016/j.is.2003.08.003en_US


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