Materialized view selection and maintenance using multi-query optimization

Materialized views have been found to be very effective at speeding up quer ies, and are increasingly being supported by commercial databases and data warehouse systems. However, whereas the amount of data entering a warehouse and the number of materialized views are rapidly increasing, the time wind ow available for maintaining materialized views is shrinking. These trends necessitate efficient techniques for the maintenance of materialized views. In this paper, we show how to find an efficient plan for the maintenance of a set of materialized views, by exploiting common subexpressions between d ifferent view maintenance expressions; In particular, we show how to effici ently select (a) expressions and indices that can be effectively shared, by transient materialization; fb) additional expressions and indices for perm anent materialization; and (c) the best maintenance plan - incremental or r ecomputation - for each view. These three decisions are highly interdepende nt, and the choice of one affects the choice of the others. We develop a fr amework that cleanly integrates the various choices in a systematic and eff icient manner. Our evaluations show that many-fold improvement in view main tenance time can be achieved using our techniques. Our algorithms can also be used to efficiently select materialized views to speed up workloads cont aining queries and updates.