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

Authors
Journal
Information Systems
0306-4379
Publisher
Elsevier
Publication Date
Volume
30
Issue
1
Identifiers
DOI: 10.1016/j.is.2003.08.003
Keywords
  • Parallel Database Systems
  • Declustering
  • Hypergraph Partitioning
  • Iterative Improvement
  • Weighted Similarity Graph
  • Max-Cut Graph Partitioning
Disciplines
  • Computer Science

Abstract

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.

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