Evaluating probabilistic queries over imprecise data
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
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ICDE '04 Proceedings of the 20th International Conference on Data Engineering
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ACM Transactions on Database Systems (TODS) - Special Issue: SIGMOD/PODS 2003
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VLDB '05 Proceedings of the 31st international conference on Very large data bases
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VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
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VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
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VLDB '07 Proceedings of the 33rd international conference on Very large data bases
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EDBT '08 Proceedings of the 11th international conference on Extending database technology: Advances in database technology
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Proceedings of the 2008 ACM SIGMOD international conference on Management of data
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Proceedings of the VLDB Endowment
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Proceedings of the VLDB Endowment
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Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
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ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
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ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
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International Journal of Web and Grid Services
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Recently, several research efforts have addressed answering skyline queries efficiently over large datasets. However, this research lacks methods to compute these queries over uncertain data, where uncertain values are represented as a range. In this paper, we define skyline queries over continuous uncertain data, and propose a novel, efficient framework to answer these queries. Query answers are probabilistic, where each object is associated with a probability value of being a query answer. Typically, users specify a probability threshold, that each returned object must exceed, and a tolerance value that defines the allowed error margin in probability calculation to reduce the computational overhead. Our framework employs an efficient two-phase query processing algorithm.