Statistic Driven Acceleration of Object-Relational Space-Partitioning Index Structures

Relational index structures, as for instance the Relational Interval Tree or the Linear Quadtree, support efficient processing of queries on top of existing object-relational database systems. Furthermore, there exist effective and efficient models to estimate the selectivity and the I/O cost in order to guide the cost-based optimizer whether and how to include these index structures 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 the access methods themselves by reducing the number of generated join partners which results in fewer 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 grouping algorithm. Our experiments on an Oracle9i database yield an average speed-up between 20% and 10,000% for spatial collision queries on the Relational Interval Tree and on the Relational Quadtree.