Koyutürk, Mehmet2016-01-082016-01-082000http://hdl.handle.net/11693/18176Cataloged from PDF version of article.Ankara : Department of Computer Engineering and the Institute of Engineering and Science of Bilkent Univ., 2000.Thesis (Master's) -- Bilkent University, 2000.Includes bibliographical refences (leaves 52-55).In very large distributed database systems, the data is declustered in order to exploit parallelism while processing a query. Declustering refers to allocating the data into multiple disks in such a way that the tuples belonging to a relation are distributed evenly across disks. There are many declustering strategies proposed in the literature, however these strategies are domain specific or have deficiencies. We propose a model that exactly fits the problem and show that iterative improvement schemes can capture detailed per-relation basis declustering objective. We provide a two phase iterative improvement based algorithm and appropriate gain functions for these algorithms. The experimental results show that the proposed algorithm provides a significant performance improvement compared to the state-of-the-art graph-partitioning based declustering strategy.xi, 55 leaves ; 30 cm.Englishinfo:eu-repo/semantics/openAccessDistributed databasesDeclusteringHypergraph partitioningMax-cut graph partitioningThesisB053701