Over the past ten to fifteen years, data warehouse platformshave grown enormously, both in terms of their importance and their sheer size. Traditionally, such systems have been based upon a dimensional model known as the Star Schema that consists of a central fact table and a series of related dimension tables. Given the enormous size of the fact table, virtually all current systems augment the primary fact table with a small number of focused summary tables. Previous research has addressed the issue of the selection or identification of the most cost-effective summaries. However, the problem of efficiently computing a given view subset has received far less attention. In this paper, we present a suite of greedy algorithms for the construction of these view subsets. Experimental results demonstrate cost savings of between 20% and 70% relative to the naive alternatives, depending upon the degree of materialization required.

Additional Metadata
Keywords data cube, OLAP, partial materialization
Persistent URL dx.doi.org/10.1145/1317331.1317343
Conference 10th ACM International Workshop on Data Warehousing and OLAP, DOLAP'07 - Co-Located with CIKM 2007
Citation
Dehne, F, Eavis, T. (Todd), & Rau-Chaplin, A. (Andrew). (2007). Efficient computation of view subsets. Presented at the 10th ACM International Workshop on Data Warehousing and OLAP, DOLAP'07 - Co-Located with CIKM 2007. doi:10.1145/1317331.1317343