VAGUE: a user interface to relational databases that permits vague queries
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The VLDB Journal — The International Journal on Very Large Data Bases
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Set similarity join on probabilistic data
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Knowledge compilation meets database theory: compiling queries to decision diagrams
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Distributed inference and query processing for RFID tracking and monitoring
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Efficient query answering in probabilistic RDF graphs
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Cost-efficient repair in inconsistent probabilistic databases
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Tiresias: the database oracle for how-to queries
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Factorised representations of query results: size bounds and readability
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FARP: Mining fuzzy association rules from a probabilistic quantitative database
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Top-K aggregate queries on continuous probabilistic datasets
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Probabilistic graph summarization
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Oblivious bounds on the probability of boolean functions
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Recognizing patterns in streams with imprecise timestamps
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Complex event processing over distributed probabilistic event streams
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A temporal-probabilistic database model for information extraction
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Top-k entities query processing on uncertainly fused multi-sensory data
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Parallel skyline queries over uncertain data streams in cloud computing environments
International Journal of Web and Grid Services
Anytime approximation in probabilistic databases
The VLDB Journal — The International Journal on Very Large Data Bases
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We describe a framework for supporting arbitrarily complex SQL queries with "uncertain" predicates. The query semantics is based on a probabilistic model and the results are ranked, much like in Information Retrieval. Our main focus is query evaluation. We describe an optimization algorithm that can compute efficiently most queries. We show, however, that the data complexity of some queries is #P-complete, which implies that these queries do not admit any efficient evaluation methods. For these queries we describe both an approximation algorithm and a Monte-Carlo simulation algorithm.