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 ABSTRACT
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.
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CITED BY 26
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Jayavel Shanmugasundaram , Usama Fayyad , P. S. Bradley, Compressed data cubes for OLAP aggregate query approximation on continuous dimensions, Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining, p.223-232, August 15-18, 1999, San Diego, California, United States
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