A Generalized Framework for Indexing OLAP Aggregates
Abstract
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)