An Alternative Storage Organization for ROLAP Aggregate Views
Based on Cubetrees
Yannis Kotidis
(University of Maryland)
Nick Roussopoulos
(University of Maryland)
The Relational On-Line Analytical Processing (ROLAP) is emerging as
the dominant approach in data warehousing with decision support
applications. In order to enhance query performance, the ROLAP
approach relies on selecting and materializing in summary tables
appropriate subsets of aggregate views which are then engaged in
speeding up OLAP queries. However, a straight forward relational
storage implementation of materialized ROLAP views is immensely
wasteful on storage and incredibly inadequate on query performance
and incremental update speed. In this paper we propose the use of
Cubetrees, a collection of packed and compressed R-trees, as
an alternative storage and index organization for ROLAP views and
provide an efficient algorithm for mapping an arbitrary set of OLAP
views to a collection of Cubetrees that achieve excellent
performance. Compared to a conventional (relational) storage
organization of materialized OLAP views, Cubetrees offer at least a
2-1 storage reduction, a 10-1 better OLAP query performance, and a
100-1 faster updates. We compare the two alternative approaches
with data generated from the TPC-D benchmark and stored in the
Informix Universal Server (IUS). The straight forward
implementation materializes the ROLAP views using IUS tables and
conventional B-tree indexing. The Cubetree implementation
materializes the same ROLAP views using a Cubetree Datablade
developed for IUS. The experiments demonstrate that the Cubetree
storage organization is superior in storage, query performance and
update speed.