Incomplete Information in Relational Databases
Journal of the ACM (JACM)
An analysis of first-order logics of probability
Artificial Intelligence
A probabilistic relational algebra for the integration of information retrieval and database systems
ACM Transactions on Information Systems (TOIS)
Query evaluation in probabilistic relational databases
Selected papers from the international workshop on Uncertainty in databases and deductive systems
ProbView: a flexible probabilistic database system
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Optimal aggregation algorithms for middleware
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Foundations of Databases: The Logical Level
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The Theory of Probabilistic Databases
VLDB '87 Proceedings of the 13th International Conference on Very Large Data Bases
Supporting Incremental Join Queries on Ranked Inputs
Proceedings of the 27th International Conference on Very Large Data Bases
Evaluating top-k queries over web-accessible databases
ACM Transactions on Database Systems (TODS)
ULDBs: databases with uncertainty and lineage
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
OLAP over uncertain and imprecise data
The VLDB Journal — The International Journal on Very Large Data Bases
The Threshold Algorithm: From Middleware Systems to the Relational Engine
IEEE Transactions on Knowledge and Data Engineering
Efficient query evaluation on probabilistic databases
The VLDB Journal — The International Journal on Very Large Data Bases
Joining ranked inputs in practice
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Supporting top-K join queries in relational databases
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Merging the results of approximate match operations
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
Probabilistic top-k and ranking-aggregate queries
ACM Transactions on Database Systems (TODS)
World-set decompositions: Expressiveness and efficient algorithms
Theoretical Computer Science
Efficient Processing of Top-k Queries in Uncertain Databases
ICDE '08 Proceedings of the 2008 IEEE 24th 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
On the semantics and evaluation of top-k queries in probabilistic databases
ICDEW '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering Workshop
Ranking queries on uncertain data
The VLDB Journal — The International Journal on Very Large Data Bases
Asymptotically efficient algorithms for skyline probabilities of uncertain data
ACM Transactions on Database Systems (TODS)
A survey on representation, composition and application of preferences in database systems
ACM Transactions on Database Systems (TODS)
Top-k best probability queries on probabilistic data
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications - Volume Part II
P-top-k queries in a probabilistic framework from information extraction models
Computers & Mathematics with Applications
Skyline queries, front and back
ACM SIGMOD Record
Top-k best probability queries and semantics ranking properties on probabilistic databases
Data & Knowledge Engineering
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We study here fundamental issues involved in top-k query evaluation in probabilistic databases. We consider simple probabilistic databases in which probabilities are associated with individual tuples, and general probabilistic databases in which, additionally, exclusivity relationships between tuples can be represented. In contrast to other recent research in this area, we do not limit ourselves to injective scoring functions. We formulate three intuitive postulates for the semantics of top-k queries in probabilistic databases, and introduce a new semantics, Global-Topk, that satisfies those postulates to a large degree. We also show how to evaluate queries under the Global-Topk semantics. For simple databases we design dynamic-programming based algorithms. For general databases we show polynomial-time reductions to the simple cases, and provide effective heuristics to speed up the computation in practice. For example, we demonstrate that for a fixed k the time complexity of top-k query evaluation is as low as linear, under the assumption that probabilistic databases are simple and scoring functions are injective.