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Relational index structures, as for instance the Relational Interval Tree, the Relational RTree, 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. The different join partners are grouped together according to a cost-based grouping algorithm. Our first experiments on an Oracle9i database yield a speed-up of up to 1,000% for the Relational Interval Tree, the Relational R-Tree and for the Linear Quadtree.