Statistical sampling to instantiate materialized view selection problems in data warehouses
Document Type
Article
Publication Date
1-1-2007
Abstract
In any online decision support system, the backbone is a data warehouse. In order to facilitate rapid response to complex business decision support queries, it is a common practice to materialize an appropriate set of the views at the data warehouse. However, it typically requires the solution of the Materialized View Selection (MVS) problem to select the right set of views to materialize in order to achieve a certain level of service given a limited amount of resource such as materialization time, storage space, or view maintenance time. Dynamic changes in the source data and the end users requirement necessitate rapid and repetitive instantiation and solution of the MVS problem. In an online decision support context, time is of the essence in finding acceptable solutions to this problem. In this chapter we have used a novel approach to instantiate and solve four versions ofthe MVS problem using three sampling techniques and two databases. We compared these solutions with the optimal solutions corresponding to the actual problems. In our experimentation, we found that the sampling approach resulted in substantial savings in time while producing good solutions. Copyright © 2007, Idea Group Inc.
Recommended Citation
Ahmed, Mesbah U.; Agrawal, Vikas; Nandkeolyar, Udaya; and Sundararaghavan, P. S., "Statistical sampling to instantiate materialized view selection problems in data warehouses" (2007). College of Business and Economics. 321.
https://digitalcommons.uncfsu.edu/college_business_economics/321