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      Iterative-improvement-based declustering heuristics for multi-disk databases

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      Author
      Koyutürk, M.
      Aykanat, Cevdet
      Date
      2005
      Source Title
      Information Systems
      Print ISSN
      0306-4379
       
      1873-6076
       
      Publisher
      Elsevier
      Volume
      30
      Issue
      1
      Pages
      47 - 70
      Language
      English
      Type
      Article
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      Abstract
      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 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.
      Keywords
      Parallel database systems
      Declustering
      Hypergraph partitioning
      Iterative improvement
      Weighted similarity graph
      Maxcut graph partitioning
      Permalink
      http://hdl.handle.net/11693/13411
      Published Version (Please cite this version)
      http://dx.doi.org/10.1016/j.is.2003.08.003
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