Distance browsing in spatial databases
ACM Transactions on Database Systems (TODS)
Proceedings of the 17th International Conference on Data Engineering
Efficient Progressive Skyline Computation
Proceedings of the 27th International Conference on Very Large Data Bases
Efficient OLAP Operations in Spatial Data Warehouses
SSTD '01 Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases
An optimal and progressive algorithm for skyline queries
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Maximal vector computation in large data sets
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Efficient computation of the skyline cube
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Catching the best views of skyline: a semantic approach based on decisive subspaces
VLDB '05 Proceedings of the 31st international conference on Very large data bases
SUBSKY: Efficient Computation of Skylines in Subspaces
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Refreshing the sky: the compressed skycube with efficient support for frequent updates
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Finding k-dominant skylines in high dimensional space
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Shooting stars in the sky: an online algorithm for skyline queries
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Probabilistic skylines on uncertain data
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Efficient skyline computation over low-cardinality domains
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Approaching the skyline in Z order
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Efficient computation of reverse skyline queries
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Efficient processing of top-k dominating queries on multi-dimensional data
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Dynamic skyline queries in metric spaces
EDBT '08 Proceedings of the 11th international conference on Extending database technology: Advances in database technology
Efficient sort-based skyline evaluation
ACM Transactions on Database Systems (TODS)
Finding the influence set through skylines
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Scalable skyline computation using object-based space partitioning
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
EDBT'06 Proceedings of the 10th international conference on Advances in Database Technology
Daisy: the center for data-intensive systems at Aalborg University
ACM SIGMOD Record
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A conventional skyline query requires no query point, and usually employs a MIN or MAX annotation only to prefer smaller or larger values on each dimension. A relative skyline query, in contrast, is issued with a combination of a query point and a set of preference annotations for all involved dimensions. Due to the relative dominance definition in a relative skyline query, there exist various such combinations which we call as user preferences. It is also often interesting to identify from an assorted user preference collection the most influential preference that leads to the largest relative skyline. We call such a problem the most influential preference query. In this paper we propose a complete set of techniques to solve such novel and useful problems within a uniform framework. We first formalize different preference annotations that can be imposed on a dimension by a relative skyline query user. We then propose an effective transformation to handle all these annotations in a uniform way. Based on the transformation, we adapt the well-established Branch-and-Bound Skyline (BBS) algorithm to process relative skyline queries with assorted user preferences. In order to process the most influential preference queries, we develop two aggregation R-tree based algorithms. We conduct extensive experiments on both real and synthetic datasets to evaluate our proposals.