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An optimal and progressive algorithm for skyline queries
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Working Models for Uncertain Data
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Probabilistic skylines on uncertain data
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
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WAIM '08 Proceedings of the 2008 The Ninth International Conference on Web-Age Information Management
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IEEE Transactions on Knowledge and Data Engineering
A Survey of Uncertain Data Algorithms and Applications
IEEE Transactions on Knowledge and Data Engineering
Semantics of Ranking Queries for Probabilistic Data and Expected Ranks
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Computing all skyline probabilities for uncertain data
Proceedings of the twenty-eighth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Top-k queries on uncertain data: on score distribution and typical answers
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Semantics and evaluation of top-k queries in probabilistic databases
Distributed and Parallel Databases
A unified approach to ranking in probabilistic databases
Proceedings of the VLDB Endowment
Robust ranking of uncertain data
DASFAA'11 Proceedings of the 16th international conference on Database systems for advanced applications - Volume Part I
Numerical Analysis for Statisticians
Numerical Analysis for Statisticians
Top-k best probability queries and semantics ranking properties on probabilistic databases
Data & Knowledge Engineering
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There has been much interest in answering top-k queries on probabilistic data in various applications such as market analysis, personalised services, and decision making. In relation to probabilistic data, the most common problem in answering top-k queries is selecting the semantics of results according to their scores and top-k probabilities. In this paper, we propose a novel top-k best probability query to obtain results which are not only the best top-k scores but also the best top-k probabilities. We also introduce an efficient algorithm for top-k best probability queries without requiring the user's defined threshold. Then, the top-k best probability answer is analysed, which satisfies the semantic ranking properties of queries [3,18] on uncertain data. The experimental studies are tested with both the real data to verify the effectiveness of the top-k best probability queries and the efficiency of our algorithm.