Adaptive filters for continuous queries over distributed data streams
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Efficient indexing methods for probabilistic threshold queries over uncertain data
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Quality-Aware Probing of Uncertain Data with Resource Constraints
SSDBM '08 Proceedings of the 20th international conference on Scientific and Statistical Database Management
Efficient search for the top-k probable nearest neighbors in uncertain databases
Proceedings of the VLDB Endowment
Cleaning uncertain data with quality guarantees
Proceedings of the VLDB Endowment
Evaluating probability threshold k-nearest-neighbor queries over uncertain data
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
A Survey of Uncertain Data Algorithms and Applications
IEEE Transactions on Knowledge and Data Engineering
A Framework for Clustering Uncertain Data Streams
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Frequent subgraph pattern mining on uncertain graph data
Proceedings of the 18th ACM conference on Information and knowledge management
Proceedings of the 18th ACM conference on Information and knowledge management
Probabilistic nearest-neighbor query on uncertain objects
DASFAA'07 Proceedings of the 12th international conference on Database systems for advanced applications
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Recently, management of uncertain data draws lots of attention to consider the granularity of devices and noises in collection and delivery of data. Previous works directly model and handle uncertain data to find the required results. However, when data uncertainty is not small or limited, users are not able to obtain useful insights and thereby tend to provide more resources to improve the solution, by reducing the uncertainty of data. In light of this issue, this paper formulates a new problem of choosing a given number of uncertain data objects for acquiring their attribute values to improve the solutions of Probabilistic k-Nearest-Neighbor (k-PNN) query. We prove that solutions must be better after data acquisition, and we devise algorithms to maximize expected improvement. Our experiment results demonstrate that the probability can be significantly improved with only a small number of data acquisitions.