Efficient skyline refinement using trade-offs respecting don't care attributes

Skyline Queries have received a lot of attention due to their intuitive query formulation. Following the concept of Pareto optimality all ‘best’ database items satisfying different aspects of the query are returned to the user. However, this often results in huge result set sizes. In everyday’s life users face the same problem. But here, when confronted with a too large variety of choices users tend to focus only on some aspects of the attribute space at a time and try to figure out acceptable compromises between these attributes. Such trade-offs are not reflected by the Pareto paradigm. Incorporating them into user preferences and adjusting skyline results accordingly thus needs special algorithms beyond traditional sky-lining. In this paper we propose a novel algorithm for efficiently incorporating such typical trade-off information into preference orders. Furthermore, we allow for “don’t care” semantics on certain attributes expressing ones indecisiveness given certain preconditions. Our experiments on both real world and synthetic data sets show the impact of our techniques: impractical skyline sizes efficiently become manageable with a minimum amount of user interaction. Additionally, we also design a method to elicit especially interesting trade-offs promising a high reduction of skyline sizes. At any point, the user can choose whether to provide individual trade-offs, or accept those suggested by the system. The benefit of incorporating tradeoffs into the strict Pareto semantics is clear: result sets become manageable, while additionally getting more focused on the users’ information needs.