Query optimization using restructured views

We study optimization of relational queries using materialized views, where views may be regular or restructured. In a restructured view, some data from the base table(s) are represented as metadata - that is, schema information, such as table and attribute names - or vice versa.Using restructured views in query optimization opens up a new spectrum of views that were not previously available, and can result in significant additional savings in query-evaluation costs. These savings can be obtained due to a significantly larger set of views to choose from, and may involve reduced table sizes, elimination of self-joins, clustering produced by restructuring, and horizontal partitioning.In this paper we propose a general query-optimization framework that treats regular and restructured views in a uniform manner and is applicable to SQL select-project-join queries and views with or without aggregation. Within the framework we provide (1) algorithms to determine when a view (regular or restructured) is usable in answering a query, and (2) algorithms to rewrite a query using usable views.Semantic information, such as knowledge of the key of a view, can be used to further optimize a rewritten query. Within our general query-optimization framework, we develop techniques for determining the key of a (regular or restructured) view, and show how this information can be used to further optimize a rewritten query. It is straightforward to integrate all our algorithms and techniques into standard query-optimization algorithms.