An Optimal Algorithm for Monte Carlo Estimation
SIAM Journal on Computing
Algorithms and Data Structures in VLSI Design
Algorithms and Data Structures in VLSI Design
ULDBs: databases with uncertainty and lineage
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Management of probabilistic data: foundations and challenges
Proceedings of the twenty-sixth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
The dichotomy of conjunctive queries on probabilistic structures
Proceedings of the twenty-sixth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
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The VLDB Journal — The International Journal on Very Large Data Bases
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Proceedings of the 2008 ACM SIGMOD international conference on Management of data
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SFCS '83 Proceedings of the 24th Annual Symposium on Foundations of Computer Science
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ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
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Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
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Computing query probability with incidence algebras
Proceedings of the twenty-ninth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
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Proceedings of the VLDB Endowment
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Proceedings of the VLDB Endowment
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Proceedings of the 14th International Conference on Database Theory
On the optimal approximation of queries using tractable propositional languages
Proceedings of the 14th International Conference on Database Theory
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Proceedings of the 14th International Conference on Database Theory
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Proceedings of the 15th International Conference on Database Theory
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Proceedings of the VLDB Endowment
The dichotomy of probabilistic inference for unions of conjunctive queries
Journal of the ACM (JACM)
Anytime approximation in probabilistic databases
The VLDB Journal — The International Journal on Very Large Data Bases
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This paper investigates the problem of efficiently computing the confidences of distinct tuples in the answers to conjunctive queries with inequalities ( Our contributions are of both theoretical and practical importance. We define a class of tractable queries with inequalities, and generalize existing results on #P-hardness of query evaluation, now in the presence of inequalities. For the tractable queries, we introduce a confidence computation technique based on efficient compilation of the lineage of the query answer into Ordered Binary Decision Diagrams (OBDDs), whose sizes are linear in the number of variables of the lineage. We implemented a secondary-storage variant of our technique in PostgreSQL. This variant does not need to materialize the OBDD, but computes, in one scan over the lineage, the probabilities of OBDD fragments and combines them on the fly. Experiments with probabilistic TPC-H data show up to two orders of magnitude improvements when compared with state-of-the-art approaches.