Decision support applications often require fast response time to a wide variety of aggregate queries extracted from huge amounts of data. In this paper we propose the use of well organized packed R-trees for storing and maintaining multidimensional aggregates. Moreover, we present a general framework for mapping OLAP data to a collection of R-trees that achieve a high degree of data clustering with very low space overhead. We then propose four different allocation strategies designed to optimize different application needs. On the second part of the paper we present experimental results on high dimensionality OLAP data (up to 10 dimensions) of realistic size. Finally we characterize the performance of the proposed allocation strategies with respect to both incremental updates and response time for a variety of different queries. (Also cross-referenced as UMIACS-TR-97-76)