Proceedings of the 17th International Conference on Data Engineering
Preference formulas in relational queries
ACM Transactions on Database Systems (TODS)
Finding k-dominant skylines in high dimensional space
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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 sort-based skyline evaluation
ACM Transactions on Database Systems (TODS)
Supporting Case-Based Retrieval by Similarity Skylines: Basic Concepts and Extensions
ECCBR '08 Proceedings of the 9th European conference on Advances in Case-Based Reasoning
Skyline Query Processing for Incomplete Data
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Database preferences queries: a possibilistic logic approach with symbolic priorities
FoIKS'08 Proceedings of the 5th international conference on Foundations of information and knowledge systems
EDBT'06 Proceedings of the 10th international conference on Advances in Database Technology
Fuzzy dominance skyline queries
DEXA'07 Proceedings of the 18th international conference on Database and Expert Systems Applications
A preference query model based on a fusion of local orders
ECSQARU'11 Proceedings of the 11th European conference on Symbolic and quantitative approaches to reasoning with uncertainty
On possibilistic skyline queries
FQAS'11 Proceedings of the 9th international conference on Flexible Query Answering Systems
Skyline probability over uncertain preferences
Proceedings of the 16th International Conference on Extending Database Technology
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This paper deals with database preference queries based on the skyline paradigm, which aim at retrieving the tuples non Paretodominated by any other. We propose different ways to fuzzify such queries in order to make them more flexible, to increase their discrimination power, to make them more drastic or more tolerant. In particular, some of these extensions make it possible to reduce the risk of getting many incomparable tuples, even when the number of dimensions is high.