A Bayesian method for query approximation

Abstract

This study presents statistical techniques to obtain local approximate query answers for aggregate multivariate materialized views thus eliminating the need for repetitive scanning of the source data. In widely distributed management information systems, detailed data do not necessarily reside in the same physical location as the decision-maker; thus, requiring scanning of the source data as needed by the query demand. Decision-making, business intelligence and data analysis could involve multiple data sources, data diversity, aggregates and large amounts of data. Management often confronts delays in information acquisition from remote sites. Management decisions usually involve analyses that require the most precise summary data available. These summaries are readily available from data warehouses and can be used to estimate or approximate data in exchange for a quicker response. An approach to supporting aggregate materialized view management is proposed that reconstructs data sets locally using posterior parameter estimates based on sufficient statistics in a log-linear model with a multinomial likelihood.

Keywords:

• query approximation
• data reduction
• materialized view management
• BIPF