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The containment rate of query Q1 in query Q2 over database D is the percentage of Q1's result tuples over D that are also in Q2's result over D.
Optimal implementation of conjunctive queries in relational data bases
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C. Chekuri and A. Rajaraman · 2000
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A. Calì · 2006
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C. Farré, W. Nutt, E. Teniente, and T. Urpí · 2007
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G. Chatzopoulou, M. Eirinaki, and N. Polyzotis · 2009
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Predicting multiple metrics for queries: Better decisions enabled by machine learning
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Preventing bad plans by bounding the impact of cardinality estimation errors
G. Moerkotte, T. Neumann, and G. Steidl · 2009
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Robust estimation of resource consumption for SQL queries using statistical techniques
J. Li, A. C. König, V. R. Narasayya, and S. Chaudhuri · 2012
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Early stopping - but when?
L. Prechelt · 2012
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Query containment in entity SQL
G. Rull, P. A. Bernstein, I. G. dos Santos, Y. Katsis, S. Melnik, and E. Teniente · 2013
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Empirical evaluation of gated recurrent neural networks on sequence modeling
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G. Aurelien · 2017
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Cardinality estimation done right: Index-based join sampling
V. Leis, B. Radke, A. Gubichev, A. Kemper, and T. Neumann · 2017
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M. A. Nielsen · 2017
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J. Brownlee · 2018
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The case for learned index structures
T. Kraska, A. Beutel, E. H. Chi, J. Dean, and N. Polyzotis · 2018
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Learning to optimize join queries with deep reinforcement learning
S. Krishnan, Z. Yang, K. Goldberg, J. M. Hellerstein, and I. Stoica · 2018
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Simple set cardinality estimation through random sampling
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Deep reinforcement learning for join order enumeration
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Learning state representations for query optimization with deep reinforcement learning
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Positive relational algebra
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Learning-based SPARQL query performance modeling and prediction
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Proceedings of the Second International Workshop on Exploiting Artificial Intelligence Techniques for Data Management, aiDM@SIGMOD
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Learned cardinalities: Estimating correlated joins with deep learning
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Cardinality estimation with local deep learning models
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