Algebraic Optimization of Relational Preference Queries

The design and implementation of advanced personalized database applications requires a preference-driven approach. Representing preferences as strict partial orders is a good choice in most practical cases. Therefore the efficient integration of preference querying into standard database technology is an important issue. We present a novel approach to relational preference query optimization based on algebraic transformations. A variety of new laws for preference relational algebra is presented. This forms the foundation for a preference query optimizer applying heuristics like ‘push preference’. A prototypical implementation and a series of benchmarks show that significant performance gains can be achieved. In summary, our results give strong evidence that by extending relational databases by strict partial order preferences one can get both: good modeling capabilities for personalization and good query runtimes. Our approach extends to recursive databases as well.