Data fluctuations, unexpected delays and frequent disconnections have
become distinctive characteristics of modern computing environments.
These new environments pose many challenges to current query processing
and optimization techniques. For example, spending considerable amount
of time optimizing a query may result in a query evaluation plan that
will soon become sub-optimal, due to the continuously changing execution
conditions. Moreover, accurate modeling of the underlying system
parameters became significantly harder in these complex, ubiquitous and
“less predictable” environments. Hence, cost parameters such as
selectivity and correlation information need to be collected and updated
in more adaptive and scalable fashion. Many emerging applications and
development environments such as IBM WebSphere will suffer from the
inadequacy of current database management systems to deal with these new
challenges.
This project studies enhancing the capabilities for current query optimization and execution to cope with the continuously changing computing environments. In particular, we investigate developing query optimization techniques that are highly adaptive to changes in the computing environment and to lack of accurate and up-to-date statistics. Moreover, we investigate developing an adaptive query execution framework that allows for continuous optimization of long running queries to respond to fluctuations and unexpected system changes. Developing a highly adaptive query optimization and execution framework will allow database management systems like DB2 to be an efficient and autonomous component in emerging applications and modern development environments