Probabilistic Similarity Search for Uncertain Time Series
SSDBM 2009 Proceedings of the 21st International Conference on Scientific and Statistical Database Management
Skyline query processing for uncertain data
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Ranking continuous probabilistic datasets
Proceedings of the VLDB Endowment
Building ranked mashups of unstructured sources with uncertain information
Proceedings of the VLDB Endowment
k-nearest neighbors in uncertain graphs
Proceedings of the VLDB Endowment
Similarity search and mining in uncertain databases
Proceedings of the VLDB Endowment
A unified approach to ranking in probabilistic databases
The VLDB Journal — The International Journal on Very Large Data Bases
Search computing
Improving web database search incorporating users query information
Proceedings of the International Conference on Web Intelligence, Mining and Semantics
Ranking with uncertain scoring functions: semantics and sensitivity measures
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Handling ER-topk query on uncertain streams
DASFAA'11 Proceedings of the 16th international conference on Database systems for advanced applications - Volume Part I
A survey on representation, composition and application of preferences in database systems
ACM Transactions on Database Systems (TODS)
Continuous inverse ranking queries in uncertain streams
SSDBM'11 Proceedings of the 23rd international conference on Scientific and statistical database management
Getting critical categories of a data set
WAIM'11 Proceedings of the 12th international conference on Web-age information management
On the semantics of top-k ranking for objects with uncertain data
Computers & Mathematics with Applications
Top-K aggregate queries on continuous probabilistic datasets
WAIM'13 Proceedings of the 14th international conference on Web-Age Information Management
Mining order-preserving submatrices from probabilistic matrices
ACM Transactions on Database Systems (TODS)
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Large databases with uncertain information are becoming more common in many applications including data integration, location tracking, and Web search. In these applications, ranking records with uncertain attributes needs to handle new problems that are fundamentally different from conventional ranking. Specifically, uncertainty in records' scores induces a partial order over records, as opposed to the total order that is assumed in the conventional ranking settings. In this paper, we present a new probabilistic model, based on partial orders, to encapsulate the space of possible rankings originating from score uncertainty. Under this model, we formulate several ranking query types with different semantics. We describe and analyze a set of efficient query evaluation algorithms. We show that our techniques can be used to solve the problem of rank aggregation in partial orders. In addition, we design novelsampling techniques to compute approximate query answers. Our experimental evaluation uses both real and synthetic data. The experimental study demonstrates the efficiency and effectiveness of our techniques in different settings.