Efficient Evaluation of Logic Programs for Querying Data Integration Systems

Abstract

Many data integration systems provide transparent access to heterogeneous data sources through a unified view of all data in terms of a global schema, which may be equipped with integrity constraints on the data. Since these constraints might be violated by the data retrieved from the sources, methods for handling such a situation are needed. To this end, recent approaches model query answering in data integration systems in terms of nonmonotonic logic programs. However, while the theoretical aspects have been deeply analyzed, there are no real implementations of this approach yet. A problem is that the reasoning tasks modeling query answering are computationally expensive in general, and that a direct evaluation on deductive database systems is infeasible for large data sets. In this paper, we investigate techniques which make user query answering by logic programs effective. We develop pruning and localization methods for the data which need to be processed in a deductive system, and a technique for the recombination of the results on a relational database engine. Experiments indicate the viability of our methods and encourage further research of this approach.