New GDM-based declustering methods for parallel range queries

Abstract

Declustering is a well known technique to achieve high performance for queries on parallel databases. We propose novel General Disk Module (GDM) based declustering algorithms, GDM Cartesian and GDM Circle, for distributing uniformly distributed multidimensional datasets to parallel disks, for datasets of any dimension. We compare the performance of the new approaches with several existing declustering algorithms, using variable numbers of disks, and with variable shapes and dimensions of the datasets. Our results show that the new approaches significantly outperform the others for almost all configurations tested.