Automated Selection of Materialized Views and Indexes for SQL Databases.

Automatically selecting an appropriate set of
materialized views and indexes for SQL
databases is a non-trivial task. A judicious choice
must be cost-driven and influenced by the
workload experienced by the system. Although
there has been work in materialized view
selection in the context of multidimensional
(OLAP) databases, no past work has looked at
the problem of building an industry-strength tool
for automated selection of materialized views
and indexes for SQL workloads. In this paper,
we present an end-to-end solution to the problem
of selecting materialized views and indexes. We
describe results of extensive experimental
evaluation that demonstrate the effectiveness of
our techniques. Our solution is implemented as
part of a tuning wizard that ships with Microsoft
SQL Server 2000.