On saying “Enough already!” in SQL
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Proceedings of the 17th International Conference on Data Engineering
Discovering strong skyline points in high dimensional spaces
Proceedings of the 14th ACM international conference on Information and knowledge management
Finding k-dominant skylines in high dimensional space
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Exploiting Indifference for Customization of Partial Order Skylines
IDEAS '06 Proceedings of the 10th International Database Engineering and Applications Symposium
Efficient processing of top-k dominating queries on multi-dimensional data
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
On Skylining with Flexible Dominance Relation
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Efficient Rewriting Algorithms for Preference Queries
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Distance-Based Representative Skyline
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Eliciting matters: controlling skyline sizes by incremental integration of user preferences
DASFAA'07 Proceedings of the 12th international conference on Database systems for advanced applications
EDBT'06 Proceedings of the 10th international conference on Advances in Database Technology
Approximately dominating representatives
ICDT'05 Proceedings of the 10th international conference on Database Theory
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The skyline of a set P of multi-dimensional points (tuples) consists of those points in P for which no clearly better point in P exists, using component-wise comparison on domains of interest. The guiding idea is to prune large data sets to a more manageable size, while ensuring that points of interest are preserved. However, when domains are only partially ordered, it easily happens that the skyline is nearly as large as the original set (or at least of the same order of magnitude), since most of the time points are incomparable in at least some dimension. To obtain a smaller, more useful skyline set which better reflects actual user preferences, we propose a richer notion of dominance, based on two assumptions: that preference specifications are often incomplete, and that actual preferences are transitive.