A data model for approximate query processing of real-time databases

Abstract

A real-time system has specific time constraints for the processing of a transaction as well as temporal consistency constraints for its temporal data. If it is not possible to produce an exact answer to a database query within the specified time constraints, for many applications it may be better to produce an approximate answer than to produce no answer at all or to wait for an exact answer and miss a deadline. Approximate query processing can be used to provide approximate answers to database queries for such applications. However, approximate query processing does not address the time dimension of the data. In this paper we extend the theoretical basis of approximate query processing to include the temporal dimension of real-time databases and present a temporal data model for approximate query processing. We describe an approximation that is designed to include the temporal data and address the temporal consistency constraints of a real-time database. We present monotone approximate relational algebra operations that are redefined to include the temporal dimension of the data. We also describe the semantic support of an implementation of an approximate query processor for temporal data that is based on this data model.