Incomplete Information in Relational Databases
Journal of the ACM (JACM)
A Probabilistic Framework for Vague Queries and Imprecise Information in Databases
VLDB '90 Proceedings of the 16th International Conference on Very Large Data Bases
ConQuer: efficient management of inconsistent databases
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
A Sampling-Based Approach to Optimizing Top-k Queries in Sensor Networks
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Probabilistic skylines on uncertain data
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Ranking queries on uncertain data: a probabilistic threshold approach
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Efficient Processing of Top-k Queries in Uncertain Databases with x-Relations
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
Consensus answers for queries over probabilistic databases
Proceedings of the twenty-eighth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
A unified approach to ranking in probabilistic databases
The VLDB Journal — The International Journal on Very Large Data Bases
An associative classifier for uncertain datasets
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Subspace top-k query processing using the hybrid-layer index with a tight bound
Data & Knowledge Engineering
Efficient pruning algorithm for top-K ranking on dataset with value uncertainty
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Top-K aggregate queries on continuous probabilistic datasets
WAIM'13 Proceedings of the 14th international conference on Web-Age Information Management
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Top-k ranking for an uncertain database is to rank tuples in it so that the best k of them can be determined. The problem has been formalized under the unified approach based on parameterized ranking functions (PRFs) and the possible world semantics. Given a PRF, one can always compute the ranking function values of all the tuples to determine the top-k tuples, which is a formidable task for large databases. In this paper, we present a general approach to pruning for the framework based on PRFs. We show a mathematical manipulation of possible worlds which reveals key insights in the part of computation that may be pruned and how to achieve it in a systematic fashion. This leads to concrete pruning methods for a wide range of ranking functions. We show experimentally the effectiveness of our approach.