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Discovering the Most Influential Sites over Uncertain Data: A Rank-Based Approach
Dec. 2012 (vol. 24 no. 12)
pp. 2156-2169
Kai Zheng, University of Queensland, Brisbane
Zi Huang, University of Queensland, Brisbane
Aoying Zhou, Institute of East China Normal University, Shanghai
Xiaofang Zhou, University of Queensland, Brisbane
With the rapidly increasing availability of uncertain data in many important applications such as location-based services, sensor monitoring, and biological information management systems, uncertainty-aware query processing has received a significant amount of research effort from the database community in recent years. In this paper, we investigate a new type of query in the context of uncertain databases, namely uncertain top-k influential sites query ({\rm UT}k{\rm IS} query for short), which can be applied in a wide range of application areas such as marketing analysis and mobile services. Since it is not so straightforward to precisely define the semantics of {\rm top}k query with uncertain data, in this paper we introduce a novel and more intuitive formulation of the query on the basis of expected rank semantics. To address the efficiency issue caused by possible worlds exploration, we propose effective pruning rules and a divide-and-conquer paradigm such that the number of candidates as well as the number of possible worlds to be considered can be significantly reduced. Finally, we conduct extensive experiments on real data sets to verify the effectiveness and efficiency of the new methods proposed in this paper.
Index Terms:
Recurrent neural networks,Databases,Pipeline processing,Semantics,Probabilistic logic,Nearest neighbor searches,Marine vehicles,top-k query,Uncertain data,reverse nearest neighbor query
Citation:
Kai Zheng, Zi Huang, Aoying Zhou, Xiaofang Zhou, "Discovering the Most Influential Sites over Uncertain Data: A Rank-Based Approach," IEEE Transactions on Knowledge and Data Engineering, vol. 24, no. 12, pp. 2156-2169, Dec. 2012, doi:10.1109/TKDE.2011.121
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