The Design and Implementation of Seeded Trees: an Eecient Method for Spatial Joins

| Existing methods for spatial joins require pre-existing spatial indices or other pre-computation, but such approaches are ineecient and limited in generality. Operand datasets of spatial joins may not all have pre-computed indices, particularly when they are dynamically generated by other selection or join operations. Also, existing spatial indices are mostly designed for spatial selections, and are not always ee-cient for joins. This paper explores the design and implementation of seeded trees 1], which are eeective for spatial joins and eecient to construct at join time. Seeded trees are R-tree-like structures, but divided into seed levels and grown levels. This structure facilitates using information regarding the join to accelerate the join process, and allows eecient buuer management. In addition to the basic structure and behavior of seeded trees, we present techniques for eecient seeded tree construction, a new buuer management strategy to lower I/O costs, and theoretical analysis for choosing algorithmic parameters. We also present methods for reducing space requirements and improving the stability of seeded tree performance with no additional I/O costs. Our performance studies show that the seeded tree method outperforms other tree-based methods by far both in terms of the number disk pages accessed and weighted I/O costs. Further, its performance gain is stable across diierent input data, and its incurred CPU penalties are also lower.