Efficient Query Processing on the Relational Quadtree

Relational index structures, as for instance the Relational Interval Tree, the Relational R-Tree, or the Linear Quadtree, support efficient processing of quer ies on top of existing object-relational database sys- tems. Furthermore, there exist effec tive and efficient models to estimate the selectivity and the I/O cost in order to guide the cost-based optimizer whether and ho w to include these index st ructures into the execution plan. By design, the models immediately fit to common extensible indexing/optimization frameworks, and their implementations exploit the built-in statistics facilities of the database server. In this paper, we show how these statistics can also be used for accelerating geo-spatial queries using the relational quadtree by re- ducing the number of generated join partners which results in less logical reads and consequently improves the overall runtime. We cut down on the number of join partners by grouping different join partners together according to a statistic driven groupi ng algorithm. Our experiments on an Oracle9i database yield an average speed-up between 30% and 300% for spatial selection queries on the Relational Quadtree.