Speeding up Warehouse Physical Design Using a Randomized Algorithm

A data warehouse stores information that is collected from multiple, heterogeneous information sources for the purpose of complex querying and analysis. Information in the warehouse is typically stored in the form of materialized views. One of the most important tasks when designing a warehouse is the selection of materialized views to be maintained in the warehouse. The goal is to select a set of views in such a way as to minimize the total query response time over all queries, given a limited amount of storage space and time for maintaining the views (view selection problem). The paper focuses on an efficient solution to the view selection problem using a genetic algorithm for computing a near-optimal set of views. Specifically, we explore the view selection problem in the context of OR view graphs. We show that our approach represents a dramatic improvement in time complexity over existing search-based approaches using heuristics. Our analysis shows that the algorithm consistently yields a solution that lies within 10% of the optimal query benefit while at the same time exhibiting only a linear increase in execution time. We have implemented a prototype version of our algorithm which is used to simulate the measurements used in the analysis of our approach.