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.
|Number of pages||9|
|State||Published - 1 Jan 1995|
|Event||Proceedings of the 1995 IEEE 11th International Conference on Data Engineering - Taipei, Taiwan|
Duration: 6 Mar 1995 → 10 Mar 1995
|Conference||Proceedings of the 1995 IEEE 11th International Conference on Data Engineering|
|Period||6/03/95 → 10/03/95|