Probabilistic top-k and ranking-aggregate queries
ACM Transactions on Database Systems (TODS)
Computing all skyline probabilities for uncertain data
Proceedings of the twenty-eighth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Threshold query optimization for uncertain data
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Identifying interesting instances for probabilistic skylines
DEXA'10 Proceedings of the 21st international conference on Database and expert systems applications: Part II
Finding the least influenced set in uncertain databases
Information Systems
Asymptotically efficient algorithms for skyline probabilities of uncertain data
ACM Transactions on Database Systems (TODS)
Database foundations for scalable RDF processing
RW'11 Proceedings of the 7th international conference on Reasoning web: semantic technologies for the web of data
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Top-k processing in uncertain databases is semantically and computationally different from traditional top-k processing. The interplay between query scores and data uncertainty makes traditional techniques inapplicable. We introduce URank, a system that processes new probabilistic formulations of top-k queries inuncertain databases. The new formulations are based on marriage of traditional top-k semantics with possible worlds semantics. URank encapsulates a new processing framework that leverages existing query processing capabilities, and implements efficient search strategies that integrate ranking on scores with ranking on probabilities, to obtain meaningful answers for top-k queries.