A similarity graph-based approach to declustering problems and its application towards parallelizing grid files

We propose a new similarity-based technique for declustering data. The proposed method can adapt to available information about query distributions, data distributions, data sizes and partition-size constraints. The method is based on max-cut partitioning of a similarity graph defined over the given set of data, under constraints on the partition sizes. It maximizes the chances that a pair of data-items that are to be accessed together by queries are allocated to distinct disks. We show that the proposed method can achieve optimal speed-up for a query-set, if there exists any other declustering method which will achieve the optimal speed-up. Experiments in parallelizing grid files show that the proposed method outperforms mapping-function-based methods for interesting query distributions as well for non-uniform data distributions