Database systems employ physical structures such as indexes and materialized views to improve query performance, potentially by orders of magnitude. It is therefore important for a database administrator to choose the appropriate configuration of these physical structures (i.e., the appropriate physical design) for a given database. Deciding on the physical design of a database is not an easy task, and a considerable amount of research exists on automatic physical design tools for relational databases. Recently, XML database systems are increasingly being used for managing highly structured XML data, and support for XML data is being added to commercial relational database systems. This raises the important question of how to choose the appropriate physical design (i.e., the appropriate set of physical structures) for an XML database. Relational automatic physical design tools are not adequate, so new research is needed in this area.
In this thesis, we address the problem of automatic physical design for XML databases, which is the process of automatically selecting the best set of physical structures for a given database and a given query workload representing the client application's usage patterns of this data. We focus on recommending two types of physical structures: XML indexes and relational materialized views of XML data. For each of these structures, we study the recommendation process and present a design advisor that automatically recommends a configuration of physical structures given an XML database and a workload of XML queries. The recommendation process is divided into four main phases: (1) enumerating candidate physical structures, (2) generalizing candidate structures in order to generate more candidates that are useful to queries that are not seen in the given workload but similar to the workload queries, (3) estimating the benefit of various candidate structures, and (4) selecting the best set of candidate structures for the given database and workload. We present a design advisor for recommending XML indexes, one for recommending materialized views, and an integrated design advisor that recommends both indexes and materialized views. A key characteristic of our advisors is that they are tightly coupled with the query optimizer of the database system, and rely on the optimizer for enumerating and evaluating physical designs whenever possible. This characteristic makes our techniques suitable for any database system that complies with a set of minimum requirements listed within the thesis. We have implemented the index, materialized view, and integrated advisors in a prototype version of IBM DB2 V9, which supports both relational and XML data, and we experimentally demonstrate the effectiveness of their
recommendations using this implementation
