Indexing multi-dimensional uncertain data with arbitrary probability density functions
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Working Models for Uncertain Data
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Model-driven data acquisition in sensor networks
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Ranking queries on uncertain data: a probabilistic threshold approach
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Query answering techniques on uncertain and probabilistic data: tutorial summary
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
A Survey of Uncertain Data Algorithms and Applications
IEEE Transactions on Knowledge and Data Engineering
Online Filtering, Smoothing and Probabilistic Modeling of Streaming data
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Top-k queries on uncertain data: on score distribution and typical answers
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
A unified approach to ranking in probabilistic databases
Proceedings of the VLDB Endowment
On pruning for top-k ranking in uncertain databases
Proceedings of the VLDB Endowment
Semantics of Ranking Queries for Probabilistic Data
IEEE Transactions on Knowledge and Data Engineering
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Top-K ranking query in uncertain databases aims to find the top-K tuples according to a ranking function. The interplay between score and uncertainty makes top-K ranking in uncertain databases an intriguing issue, leading to rich query semantics. Recently, a unified ranking framework based on parameterized ranking functions (PRFs) is formulated, which generalizes many previously proposed ranking semantics. Under the PRFs based ranking framework, efficient pruning approach for Top-K ranking on dataset with tuple uncertainty has been well studied in the literature. However, this cannot be applied to top-K ranking on dataset with value uncertainty (described through attribute-level uncertain data model), which are often natural and useful in analyzing uncertain data in many applications. This paper aims to develop efficient pruning techniques for top-K ranking on dataset with value uncertainty under the PRFs based ranking framework, which has not been well studied in the literature. We present the mathematics of deriving the pruning techniques and the corresponding algorithms. The experimental results on both real and synthetic data demonstrate the effectiveness and efficiency of the proposed pruning techniques.