Optimal file distribution for partial match retrieval
SIGMOD '88 Proceedings of the 1988 ACM SIGMOD international conference on Management of data
Disk Allocation Methods Using Error Correcting Codes
IEEE Transactions on Computers
Partitioning similarity graphs: a framework for declustering problems
Information Systems
Disk allocation for Cartesian product files on multiple-disk systems
ACM Transactions on Database Systems (TODS)
(Almost) optimal parallel block access to range queries
PODS '00 Proceedings of the nineteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
PDIS '93 Proceedings of the second international conference on Parallel and distributed information systems
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
A Hypergraph Based Approach to Declustering Problems
Distributed and Parallel Databases
Improving the Query Performance of High-Dimensional Index Structures by Bulk-Load Operations
EDBT '98 Proceedings of the 6th International Conference on Extending Database Technology: Advances in Database Technology
Cyclic Allocation of Two-Dimensional Data
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
The Idea of De-Clustering and its Applications
VLDB '86 Proceedings of the 12th International Conference on Very Large Data Bases
Declustering Spatial Objects by Clustering for Parallel Disks
DEXA '01 Proceedings of the 12th International Conference on Database and Expert Systems Applications
Multidimensional Declustering Schemes Using Golden Ratio and Kronecker Sequences
IEEE Transactions on Knowledge and Data Engineering
Declustering Using Golden Ratio Sequences
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
New GDM-Based Declustering Methods for Parallel Range Queries
IDEAS '99 Proceedings of the 1999 International Symposium on Database Engineering & Applications
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Most of the previous work on declustering have been focused on proposing good mapping functions under the assumption that the data space is partitioned equally for all dimensions. In this paper, we relax equal partition restriction on all dimensions by choosing smaller number of dimensions as split axes and study the effects of grid-like partitioning methods on the performance of a mapping function which is widely used for declustering algorithms. For this, we propose a cost model to expect the number of grid cells intersecting a range query and apply the best mapping scheme so far to the partitioned grid cells. Experiments show that our cost model gives remarkable accuracy for all ranges of selectivities and dimensions. By applying different partitioning schemes on the Kronecker sequence mapping function [5], which is known to be the best mapping function for high-dimensional data so far, we can achieve up to 23 times performance gain. Thus we can conclude that the performance of a mapping function is highly dependent on partitioning schemes applied. And our cost model gives clear criteria on how to select the number of split dimensions out of d dimensions to achieve better performance of a mapping function on declustering.