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Information Systems 30 (2005) 4770 Iterative-improvement-based declustering heuristics

Summary: Information Systems 30 (2005) 4770
Iterative-improvement-based declustering heuristics
for multi-disk databases$, $$
Mehmet Koyut.urka,1
, Cevdet Aykanatb,
Department of Computer Sciences, Purdue University, West Lafayette, IN 47907, USA
Computer Engineering Department, Bilkent University, Ankara 06800, Turkey
Received 10 July 2002; received in revised form 21 May 2003; accepted 29 August 2003
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.


Source: Aykanat, Cevdet - Department of Computer Engineering, Bilkent University


Collections: Computer Technologies and Information Sciences