Incomplete Information in Relational Databases
Journal of the ACM (JACM)
ACM Transactions on Database Systems (TODS)
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The VLDB Journal — The International Journal on Very Large Data Bases
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Working Models for Uncertain Data
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Journal of Computer and System Sciences
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VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Efficient query evaluation on probabilistic databases
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Materialized views in probabilistic databases: for information exchange and query optimization
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Exploiting Lineage for Confidence Computation in Uncertain and Probabilistic Databases
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
World-set decompositions: expressiveness and efficient algorithms
ICDT'07 Proceedings of the 11th international conference on Database Theory
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EDBT'06 Proceedings of the 10th international conference on Advances in Database Technology
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ACM Transactions on Database Systems (TODS)
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Certain conjunctive query answering in SQL
SUM'12 Proceedings of the 6th international conference on Scalable Uncertainty Management
Charting the tractability frontier of certain conjunctive query answering
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Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
A dichotomy in the complexity of counting database repairs
Journal of Computer and System Sciences
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We review in this paper some recent yet fundamental results on evaluating queries over probabilistic databases. While one can see this problem as a special instance of general purpose probabilistic inference, we describe in this paper two key database specific techniques that significantly reduce the complexity of query evaluation on probabilistic databases. The first is the separation of the query and the data: we show here that by doing so, one can identify queries whose data complexity is #P-hard, and queries whose data complexity is in PTIME. The second is the aggressive use of previously computed query results (materialized views): in particular, by rewriting a query in terms of views, one can reduce its complexity from #P-complete to PTIME. We describe a notion of a partial representation for views, and show that, once computed and stored, this partial representation can be used to answer subsequent queries on the probabilistic databases. evaluation.