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The onion technique: indexing for linear optimization queries
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PREFER: a system for the efficient execution of multi-parametric ranked queries
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Minimal probing: supporting expensive predicates for top-k queries
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Top-k selection queries over relational databases: Mapping strategies and performance evaluation
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R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
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Proceedings of the 2003 ACM SIGMOD international conference on Management of data
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Algorithms and applications for answering ranked queries using ranked views
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VLDB '05 Proceedings of the 31st international conference on Very large data bases
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Working Models for Uncertain Data
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Branch-and-bound processing of ranked queries
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VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
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Probabilistic skylines on uncertain data
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VLDB '07 Proceedings of the 33rd international conference on Very large data bases
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DASFAA'07 Proceedings of the 12th international conference on Database systems for advanced applications
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Proceedings of the 2008 ACM SIGMOD international conference on Management of data
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Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Top-k dominating queries in uncertain databases
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Efficient processing of probabilistic reverse nearest neighbor queries over uncertain data
The VLDB Journal — The International Journal on Very Large Data Bases
Computing all skyline probabilities for uncertain data
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Ranking distributed probabilistic data
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Efficient join processing on uncertain data streams
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Reverse skyline search in uncertain databases
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Skyline query processing for uncertain data
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Efficient fuzzy top-k query processing over uncertain objects
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The VLDB Journal — The International Journal on Very Large Data Bases
Ranking queries on uncertain data
The VLDB Journal — The International Journal on Very Large Data Bases
Asymptotically efficient algorithms for skyline probabilities of uncertain data
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Shooting top-k stars in uncertain databases
The VLDB Journal — The International Journal on Very Large Data Bases
Efficient fuzzy ranking queries in uncertain databases
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Probabilistic top-k dominating queries in uncertain databases
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Provisional reporting for rank joins
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Data & Knowledge Engineering
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Recently, many new applications, such as sensor data monitoring and mobile device tracking, raise up the issue of uncertain data management. Compared to "certain" data, the data in the uncertain database are not exact points, which, instead, often locate within a region. In this paper, we study the ranked queries over uncertain data. In fact, ranked queries have been studied extensively in traditional database literature due to their popularity in many applications, such as decision making, recommendation raising, and data mining tasks. Many proposals have been made in order to improve the efficiency in answering ranked queries. However, the existing approaches are all based on the assumption that the underlying data are exact (or certain). Due to the intrinsic differences between uncertain and certain data, these methods are designed only for ranked queries in certain databases and cannot be applied to uncertain case directly. Motivated by this, we propose novel solutions to speed up the probabilistic ranked query (PRank) over the uncertain database. Specifically, we introduce two effective pruning methods, spatial and probabilistic, to help reduce the PRank search space. Then, we seamlessly integrate these pruning heuristics into the PRank query procedure. Extensive experiments have demonstrated the efficiency and effectiveness of our proposed approach in answering PRank queries, in terms of both wall clock time and the number of candidates to be refined.