Effective Cost Models for Predicting Web Query Execution Cost

Abstract

Classical query optimizers rely on sophisticated cost models to estimate the cost of executing a query and its operators. By using this cost model, an efficient global plan is created by the optimizer which will be used to execute a given query. This cost modeling facility is difficult to be implemented in Web query engines because many local data sources might not be comfortable in sharing meta data information due to confidentiality issues. In this work, an efficient and effective cost modeling techniques for Web query engines are proposed. These techniques does not force the local data sources to reveal their meta data but employs a learning mechanism to estimate the cost of executing a given local query. Two cost modeling algorithms namely: Poisson cost model and Exponential cost model algorithms are presented. Empirical results over real world datasets reveal the efficiency and effectiveness of the new cost models.