Threshold-based declustering

Abstract

Declustering techniques reduce query response time 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 has focused on finding schemes with low worst case additive error. However, additive error based schemes have many limitations including lack of progressive guarantees and existence of small non-optimal queries. In this paper, we take a different approach and propose threshold-based declustering. We investigate the threshold k such that all spatial range queries with ⩽ k buckets are optimal. Upper bound on threshold is analyzed using bound diagrams and a number theoretic algorithm is proposed to find schemes with high threshold value. Threshold-based schemes have many advantages: they have low worst-case additive error, provide progressive guarantees by dividing larger queries into subqueries with ⩽ k buckets, can be used to compare replicated declustering schemes and render many large complementary queries optimal.