Restricting skyline sizes using weak Pareto dominance

摘要: Skyline queries have recently received a lot of attention due to their intuitive query formulation: users can state preferences with respect several attributes. Unlike numerical or score-based preferences, over discrete value domains do not show an inherent total order, but rely on partial orders as stated by the user. In such typically many object values are incomparable, increasing size skyline sets significantly, and making computation expensive. this paper we explore how enable interactive tasks like refinement relevance feedback providing interesting subsets full Pareto skyline, which give good overview skyline. To be practical these small, efficient compute, suitable for higher numbers predicates, representative. The key improved performance reduced result set sizes is relaxation semantics concept weak dominance. We argue that yields results it opens up use scalable processing algorithms. first derive complete subset given dominance called ‘restricted skyline’ then considering individual objects limit further ‘focused skyline’. Assessing impact our experiments approach indeed leads lean outperforms computations two magnitude.