Self-Tuning Database Systems

It is common knowledge that the modern world generates and stores information at an increasing rate. A typical example is the emerging field of e-science, where the adoption of high-performance computing has led to the generation of vast volumes of scientific data. The usefulness of the stored information, of course, depends crucially on the ability to query it effectively, and hence on the use of state of the art data management technology. Unfortunately, despite the growing trends of data collection, the adoption of database systems in e-science has been less than spectacular and has often lead to a lot of frustration. One important reason can be traced to the complexity of managing and tuning a database system. Essentially, the performance of a database system depends crucially on its physical schema, that is, the set of physical structures, such as indices and materialized views, that can speed up the execution of queries. In order to be effective, of course, the physical schema has to match the traits of the workload, e.g., it must index more heavily the parts of the database that are frequently referenced in queries. At the same time, the schema is typically constrained by a total disk space budget for its storage. Thus, designing an effective physical schema involves a non-trivial optimization problem: maximize the throughput of the query processor, assuming limited resources for storing the materialized structures. Clearly, a scientist (or, for that matter, any non-expert user) is no