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We describe a new deep learning approach to cardinality estimation.
Approximation by superpositions of a sigmoidal function
G. Cybenko · 1989
Earlier work this paper cites.
Selectivity estimation without the attribute value independence assumption
V. Poosala and Y. E. Ioannidis · 1997
Earlier work this paper cites.
Selectivity estimation in extensible databases - A neural network approach
M. S. Lakshmi and S. Zhou · 1998
Earlier work this paper cites.
End-biased samples for join cardinality estimation
C. Estan and J. F. Naughton · 2006
Earlier work this paper cites.
A black-box approach to query cardinality estimation
T. Malik, R. C. Burns, and N. V. Chawla · 2007
Earlier work this paper cites.
Predicting multiple metrics for queries: Better decisions enabled by machine learning
A. Ganapathi, H. A. Kuno, U. Dayal, J. L. Wiener, A. Fox, M. I. Jordan, and D. A. Patterson · 2009
Earlier work this paper cites.
Preventing bad plans by bounding the impact of cardinality estimation errors
G. Moerkotte, T. Neumann, and G. Steidl · 2009
Earlier work this paper cites.
HyPer: A Hybrid OLTP & OLAP Main Memory Database System Based on Virtual Memory Snapshots
A. Kemper and T. Neumann · 2011
Earlier work this paper cites.
Learning-based query performance modeling and prediction
M. Akdere, U. Çetintemel, M. Riondato, E. Upfal, and S. B. Zdonik · 2012
Earlier work this paper cites.
Robust estimation of resource consumption for SQL queries using statistical techniques
J. Li, A. C. König, V. R. Narasayya, and S. Chaudhuri · 2012
Earlier work this paper cites.
Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2014
Earlier work this paper cites.
Is query optimization a solved problem?
G. Lohman · 2014
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How good are query optimizers, really?
V. Leis, A. Gubichev, A. Mirchev, P. Boncz, A. Kemper, and T. Neumann · 2015
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Cardinality estimation using neural networks
H. Liu, M. Xu, Z. Yu, V. Corvinelli, and C. Zuzarte · 2015
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Join size estimation subject to filter conditions
D. Vengerov, A. C. Menck, M. Zaït, and S. Chakkappen · 2015
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Overcoming catastrophic forgetting in neural networks
J. Kirkpatrick, R. Pascanu, N. C. Rabinowitz, J. Veness, G. Desjardins, A. A. Rusu, K. Milan, J. Quan, T. Ramalho, A. Grabska-Barwinska, D. Hassabis, C. Clopath, D. Kumaran, and R. Hadsell · 2016
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Rethinking the inception architecture for computer vision
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna · 2016
Simple and scalable predictive uncertainty estimation using deep ensembles
B. Lakshminarayanan, A. Pritzel, and C. Blundell · 2017
Later among the works it cites.
Cardinality estimation done right: Index-based join sampling
V. Leis, B. Radke, A. Gubichev, A. Kemper, and T. Neumann · 2017
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Deep sets
M. Zaheer, S. Kottur, S. Ravanbakhsh, B. Poczos, R. R. Salakhutdinov, and A. J. Smola · 2017
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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. Hellerstein, and I. Stoica · 2018
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Query optimization through the looking glass, and what we found running the Join Order Benchmark
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Sampling-based query re-optimization
W. Wu, J. F. Naughton, and H. Singh · 2016
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Automatic database management system tuning through large-scale machine learning
D. V. Aken, A. Pavlo, G. J. Gordon, and B. Zhang · 2017
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Two-level sampling for join size estimation
Y. Chen and K. Yi · 2017
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On calibration of modern neural networks
C. Guo, G. Pleiss, Y. Sun, and K. Q. Weinberger · 2017
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What uncertainties do we need in bayesian deep learning for computer vision?
A. Kendall and Y. Gal · 2017
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V. Leis, B. Radke, A. Gubichev, A. Mirchev, P. Boncz, A. Kemper, and T. Neumann · 2018
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Deep reinforcement learning for join order enumeration
R. Marcus and O. Papaemmanouil · 2018
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Improved selectivity estimation by combining knowledge from sampling and synopses
M. Müller, G. Moerkotte, and O. Kolb · 2018
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Adaptive optimization of very large join queries
T. Neumann and B. Radke · 2018
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Learning state representations for query optimization with deep reinforcement learning
J. Ortiz, M. Balazinska, J. Gehrke, and S. S. Keerthi · 2018
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