On the representation and querying of sets of possible worlds
SIGMOD '87 Proceedings of the 1987 ACM SIGMOD international conference on Management of data
A probabilistic relational algebra for the integration of information retrieval and database systems
ACM Transactions on Information Systems (TOIS)
ProbView: a flexible probabilistic database system
ACM Transactions on Database Systems (TODS)
Querying Imprecise Data in Moving Object Environments
IEEE Transactions on Knowledge and Data Engineering
Working Models for Uncertain Data
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Efficient query evaluation on probabilistic databases
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
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
Sliding-window top-k queries on uncertain streams
Proceedings of the VLDB Endowment
Efficient search for the top-k probable nearest neighbors in uncertain databases
Proceedings of the VLDB Endowment
Evaluating probability threshold k-nearest-neighbor queries over uncertain data
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Probabilistic Verifiers: Evaluating Constrained Nearest-Neighbor Queries over Uncertain Data
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Probabilistic Skyline Operator over Sliding Windows
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Semantics of Ranking Queries for Probabilistic Data and Expected Ranks
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Probabilistic Reverse Nearest Neighbor Queries on Uncertain Data
IEEE Transactions on Knowledge and Data Engineering
Probabilistic nearest-neighbor query on uncertain objects
DASFAA'07 Proceedings of the 12th international conference on Database systems for advanced applications
A unified approach to ranking in probabilistic databases
The VLDB Journal — The International Journal on Very Large Data Bases
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This paper studies a new query on uncertain data, called k-selection query. Given an uncertain dataset of N objects, where each object is associated with a preference score and a presence probability, a k-selection query returns k objects such that the expected score of the “best available” objects is maximized. This query is useful in many application domains such as entity web search and decision making. In evaluating k-selection queries, we need to compute the expected best score (EBS) for candidate k-selection sets and search for the optimal selection set with the highest EBS. Those operations are costly due to the extremely large search space. In this paper, we identify several important properties of k-selection queries, including EBS decomposition, query recursion, and EBS bounding. Based upon these properties, we first present a dynamic programming (DP) algorithm that answers the query in O(k·N) time. Further, we propose a Bounding-and-Pruning (BP) algorithm, that exploits effective search space pruning strategies to find the optimal selection without accessing all objects. We evaluate the DP and BP algorithms using both synthetic and real data. The results show that the proposed algorithms outperform the baseline approach by several orders of magnitude.