Grant: Discovery Grants Program - Individual

The query optimizer, as one of the most important and most complex database system components, is key to the management of large databases. Its major task, query optimization, is to choose the best way to execute a query. Often there are several different ways (execution plans) to perform a given query, which might differ in the indices used, the orders of operators executed, etc., but all lead to the same correct result. The query optimizer generates and examines the candidate execution plans, and makes the decision as to which plan to follow for execution. The quality of the optimizer is very important, because costs of plans can vary dramatically and choosing a bad plan might result in extremely poor performance. Most existing approaches to query optimization aim at choosing the execution plan with the least estimated cost. However, in many cases, this is insufficient, as other qualities, such as robustness and predictability, can be equally important. Like many real-life decision making problems, query optimization inherently involves risks and uncertainties, which must be taken into consideration when choosing a plan. The primary goal of our research is to develop systems, methods, and techniques that can effectively utilize various sources of information available in a database system, in order to reduce the risk and uncertainty of query optimization decisions, and make query processing more robust and predictable.