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Supporting various top-k queries over uncertain datasets |
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| Authors: | Wenfeng Li Zufa Fu Liwei Wang Deyi Li Zhiyong Peng |
| Institution: | 1. State Key Laboratory of Software Engineering, Wuhan University, Wuhan, 430072, Hubei, China 2. School of Computer, Wuhan University, Wuhan, 430072, Hubei, China 3. International School of Software, Wuhan University, Wuhan, 430079, Hubei, China 4. Institute of Electronic System Engineering of China, Beijing, 100840, China |
| Abstract: | There have been many researches and semantics in answering top-k queries on uncertain data in various applications. However, most of these semantics must consume much of their time in computing position probability. Our approach to support various top-k queries is based on position probability distribution (PPD) sharing. In this paper, a PPD-tree structure and several basic operations on it are proposed to support various top-k queries. In addition, we proposed an approximation method to improve the efficiency of PPD generation. We also verify the effectiveness and efficiency of our approach by both theoretical analysis and experiments. |
| Keywords: | top-k queries uncertain data position probability distribution |
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