Threshold Based Declustering in High Dimensions

Declustering techniques reduce query response times through parallel I/O by distributing data among multiple devices. Except for a few cases it is not possible to find declustering schemes that are optimal for all spatial range queries. As a result of this, most of the research on declustering have focused on finding schemes with low worst case additive error. Recently, constrained declustering that maximizes the threshold k such that all spatial range queries ≤ k buckets are optimal is proposed. In this paper, we extend constrained declustering to high dimensions. We investigate high dimensional bound diagrams that are used to provide upper bound on threshold and propose a method to find good threshold-based declustering schemes in high dimensions. We show that using replicated declustering with threshold N, low worst case additive error can be achieved for many values of N. In addition, we propose a framework to find thresholds in replicated declustering.