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

Kritik der Giddensschen Sozialtheorie: e. Beitrag zur theoretisch-methodischen Grundlegung d. Sozialwissenschaften

Kießling B. Kritik der Giddensschen Sozialtheorie: e. Beitrag zur theoretisch-methodischen Grundlegung d. Sozialwissenschaften. Beiträge zur Gesellschaftsforschung ; 8. Frankfurt am Main: Lang; 1988

Optimizing Preference Queries for Personalized Web Services

Personalization of Web services requires a powerful preference model that smoothly and efficiently integrates with standard database query languages. We make the case for preferences as strict partial orders, supported in Preference SQL and Preference XPATH. Performance of Web services will crucially depend on various architectural design decisions. We pointed out that a central server architecture is desirable. Concerning the implementation of preference queries we investigated the tightly coupled architecture, presenting a novel approach for algebraic optimization based on preference algebra. We provided new transformation laws and gave evidence for the power of this heuristic optimization. This forms the basis for a new preference query optimization methodology, promising sufficient performance even for complex Web services

The "Theory of Structuration" - An Interview with Anthony Giddens

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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 modelling capabilities for personalization and good query runtimes. Our approach extends to recursive databases as well

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