Customized Answers to Summary Queries via Aggregate Views

Statistical users typically require summary tables and want fast and accurate answers to their queries. Usually, the query system keeps materialized aggregate views to speed up the evaluation of summary queries. If the summary table on the variable of interest to a statistical user is not derivable from the set of materialized aggregate views, the answer to his query will consist of an estimate and, if the user is a domain expert, he would like to participate in the estimation process. Therefore, he should be left the possibility of "tuning" the response to an auxiliary variable, for which either there is a materialized aggregate view or aggregate data can be externally provided by the user himself. In this framework, we solve the computational problems related to the estimation of summary queries, and propose efficient algorithms which make use of notions and techniques developed in the theory of acyclic database schemes.