Spatial Joins Using Seeded Trees

Abstract

Existing methods for spatial joins assume the existence of indices for the participating data sets. This assumption is not realistic for applications involving multiple map layer overlays or for queries involving non-spatial selections. In this paper, we explore a spatial join method that dynamically constructs index trees called <i>seeded trees</i> at join time. This methods uses knowledge of the data sets involved in the join process. Seeded trees are R-tree like structures, and are divided into the <i>seed levels</i> and the <i>grown levels</i>. The nodes in the seed levels are used to guide tree growth during tree construction. The seed levels can also be used to filter out some input data during construction, thereby reducing tree size. We develop a technique that uses intermediate linked lists during tree construction and significantly speeds up the tree construction process. The technique allows a large number of random disk accesses during tree construction to be replaced by smaller numbers of sequential accesses. Our performance studies show that spatial joins using seeded trees outperform those using other methods significantly in terms of disk I/O. The CPU penalties incurred are also lower except when seed-level filtering is used.