Koyutürk, M.Aykanat, Cevdet2015-07-282015-07-2820050306-43791873-6076http://hdl.handle.net/11693/13411Data 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.EnglishParallel database systemsDeclusteringHypergraph partitioningIterative improvementWeighted similarity graphMaxcut graph partitioningArticle10.1016/j.is.2003.08.003