In multi-dimensional database environments, we typically require effective indexing mechanisms for all but the smallest data sets. While numerous such methods have been proposed, the R-tree has emerged as one of the most common and reliable indexing models. Nevertheless, as user queries grow in terms of both size and dimensionality, R-tree performance can deteriorate significantly. In some application areas, however, it is possible to exploit data and query specific features to obtain dramatic improvements in query performance. We propose a variation of the classic R-tree that specifically targets data warehousing architectures. The new model not only improves performance on common user-defined range queries, but gracefully degrades to a linear scan of the data on pathologically large queries. Experimental results demonstrate reductions in disk seeks of more than 50% relative to more conventional R-tree designs.