Fetching the paper…
Reading the bibliography…
Query optimization remains one of the most challenging problems in data management systems.
On the Likelihood that One Unknown Probability Exceeds Another in View of the Evidence of Two Samples
W. R. Thompson · 1933
Earlier work this paper cites.
Access Path Selection in a Relational Database Management System
P. G. Selinger, M. M. Astrahan, D. D. Chamberlin, R. A. Lorie, and T. G. Price · 1979
Earlier work this paper cites.
The Need for Biases in Learning Generalizations
T. M. Mitchell · 1980
Earlier work this paper cites.
Bagging Predictors
L. Breiman · 1996
Earlier work this paper cites.
Join Order Selection (Good Enough Is Easy)
F. Waas and A. Pellenkoft · 2000
Earlier work this paper cites.
LEO - DB2’s LEarning Optimizer
M. Stillger, G. M. Lohman, V. Markl, and M. Kandil · 2001
Earlier work this paper cites.
Visual COKO: A debugger for query optimizer development
D. J. Abadi and M. Cherniack · 2002
Earlier work this paper cites.
Action Elimination and Stopping Conditions for the Multi-Armed Bandit and Reinforcement Learning Problems
E. Even-Dar, S. Mannor, and Y. Mansour · 2006
Earlier work this paper cites.
DeWitt clauses: Can we protect purchasers without hurting Microsoft
A. G. Read · 2006
Earlier work this paper cites.
A Reinforcement Learning Approach for Adaptive Query Processing
K. Tzoumas, T. Sellis, and C. Jensen · 2008
Earlier work this paper cites.
The Picasso database query optimizer visualizer
J. R. Haritsa · 2010
Earlier work this paper cites.
An empirical evaluation of Thompson sampling
O. Chapelle and L. Li · 2011
Earlier work this paper cites.
Deep Sparse Rectifier Neural Networks
X. Glorot, A. Bordes, and Y. Bengio · 2011
Earlier work this paper cites.
Thompson sampling: An asymptotically optimal finite-time analysis
E. Kaufmann, N. Korda, and R. Munos · 2012
Earlier work this paper cites.
Robust estimation of resource consumption for SQL queries using statistical techniques
J. Li, A. C. König, V. Narasayya, and S. Chaudhuri · 2012
Earlier work this paper cites.
Further Optimal Regret Bounds for Thompson Sampling
S. Agrawal and N. Goyal · 2013
Earlier work this paper cites.
Is Query Optimization a ‘”Solved” Problem?
G. Lohman · 2014
Earlier work this paper cites.
Devel-op: An optimizer development environment
Z. Peng, M. Cherniack, and O. Papaemmanouil · 2014
Earlier work this paper cites.
An information-theoretic analysis of Thompson sampling
D. Russo and B. V. Roy · 2014
Earlier work this paper cites.
A Better Measure of Relative Prediction Accuracy for Model Selection and Model Estimation
C. Tofallis · 2014
Earlier work this paper cites.
Adam: A Method for Stochastic Optimization
D. P. Kingma and J. Ba · 2015
Earlier work this paper cites.
How Good Are Query Optimizers, Really?
V. Leis, A. Gubichev, A. Mirchev, P. Boncz, A. Kemper, and T. Neumann · 2015
Earlier work this paper cites.
Cardinality Estimation Using Neural Networks
H. Liu, M. Xu, Z. Yu, V. Corvinelli, and C. Zuzarte · 2015
Cited alongside, same era.
Bootstrapped Thompson Sampling and Deep Exploration
I. Osband and B. Van Roy · 2015
Cited alongside, same era.
J. L. Ba, J. R. Kiros, and G. E. Hinton · 2016
Cited alongside, same era.
Deep Reinforcement Learning in Large Discrete Action Spaces
G. Dulac-Arnold, R. Evans, H. van Hasselt, P. Sunehag, T. Lillicrap, J. Hunt, T. Mann, T. Weber, T. Degris, and B. Coppin · 2016
Cited alongside, same era.
Convolutional Neural Networks over Tree Structures for Programming Language Processing
L. Mou, G. Li, L. Zhang, T. Wang, and Z. Jin · 2016
Cited alongside, same era.
Deep Reinforcement Learning for Join Order Enumeration
R. Marcus and O. Papaemmanouil · 2018
Later among the works it cites.
Learning State Representations for Query Optimization with Deep Reinforcement Learning
J. Ortiz, M. Balazinska, J. Gehrke, and S. S. Keerthi · 2018
Later among the works it cites.
SLAOrchestrator: Reducing the Cost of Performance SLAs for Cloud Data Analytics
J. Ortiz, B. Lee, M. Balazinska, J. Gehrke, and J. L. Hellerstein · 2018
Later among the works it cites.
QuickSel: Quick Selectivity Learning with Mixture Models
Y. Park, S. Zhong, and B. Mozafari · 2018
Later among the works it cites.
Deep Bayesian Bandits Showdown: An empirical comparison of bayesian deep networks for thompson sampling
C. Riquelme, G. Tucker, and J. Snoek · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
PerfEnforce: A Dynamic Scaling Engine for Analytics with Performance Guarantees
J. Ortiz, B. Lee, M. Balazinska, and J. L. Hellerstein · 2016
Cited alongside, same era.
A Survey on Contextual Multi-armed Bandits
L. Zhou · 2016
Cited alongside, same era.
Query optimization through the looking glass, and what we found running the Join Order Benchmark
V. Leis, B. Radke, A. Gubichev, A. Mirchev, P. Boncz, A. Kemper, and T. Neumann · 2017
Cited alongside, same era.
Elastic management of cloud applications using adaptive reinforcement learning
K. Lolos, I. Konstantinou, V. Kantere, and N. Koziris · 2017
Cited alongside, same era.
Releasing Cloud Databases from the Chains of Performance Prediction Models
R. Marcus and O. Papaemmanouil · 2017
Cited alongside, same era.
Provenance-Aware Query Optimization
X. Niu, R. Kapoor, B. Glavic, D. Gawlick, Z. H. Liu, V. Krishnaswamy, and V. Radhakrishnan · 2017
Cited alongside, same era.
Self-Driving Database Management Systems
A. Pavlo, G. Angulo, J. Arulraj, H. Lin, J. Lin, L. Ma, P. Menon, T. C. Mowry, M. Perron, I. Quah, S. Santurkar, A. Tomasic, S. Toor, D. V. Aken, Z. Wang, Y. Wu, R. Xian, and T. Zhang · 2017
Cited alongside, same era.
M. Schaarschmidt, A. Kuhnle, B. Ellis, K. Fricke, F. Gessert, and E. Yoneki · 2018
Later among the works it cites.
SkinnerDB: Regret-bounded Query Evaluation via Reinforcement Learning
I. Trummer, S. Moseley, D. Maram, S. Jo, and J. Antonakakis · 2018
Later among the works it cites.
A Zero-Positive Learning Approach for Diagnosing Software Performance Regressions
M. Alam, J. Gottschlich, N. Tatbul, J. S. Turek, T. Mattson, and A. Muzahid · 2019
Later among the works it cites.
AI Meets AI: Leveraging Query Executions to Improve Index Recommendations
B. Ding, S. Das, R. Marcus, W. Wu, S. Chaudhuri, and V. R. Narasayya · 2019
Later among the works it cites.
Improved Cardinality Estimation by Learning Queries Containment Rates
R. Hayek and O. Shmueli · 2019
Later among the works it cites.
Learning when to garbage collect with random forests
N. Jacek and J. E. B. Moss · 2019
Later among the works it cites.
Learned Cardinalities: Estimating Correlated Joins with Deep Learning
A. Kipf, T. Kipf, B. Radke, V. Leis, P. Boncz, and A. Kemper · 2019
Later among the works it cites.
SageDB: A Learned Database System
T. Kraska, M. Alizadeh, A. Beutel, Ed Chi, Ani Kristo, Guillaume Leclerc, Samuel Madden, Hongzi Mao, and Vikram Nathan · 2019
Later among the works it cites.
Neo: A Learned Query Optimizer
R. Marcus, P. Negi, H. Mao, C. Zhang, M. Alizadeh, T. Kraska, O. Papaemmanouil, and N. Tatbul · 2019
Later among the works it cites.
Plan-Structured Deep Neural Network Models for Query Performance Prediction
R. Marcus and O. Papaemmanouil · 2019
Later among the works it cites.
An Empirical Analysis of Deep Learning for Cardinality Estimation
J. Ortiz, M. Balazinska, J. Gehrke, and S. S. Keerthi · 2019
Later among the works it cites.
An end-to-end learning-based cost estimator
J. Sun and G. Li · 2019
Later among the works it cites.
Cardinality estimation with local deep learning models
L. Woltmann, C. Hartmann, M. Thiele, D. Habich, and W. Lehner · 2019
Later among the works it cites.
Deep unsupervised cardinality estimation
Z. Yang, E. Liang, A. Kamsetty, C. Wu, Y. Duan, X. Chen, P. Abbeel, J. M. Hellerstein, S. Krishnan, and I. Stoica · 2019
Later among the works it cites.
NeuroVectorizer: End-to-End Vectorization with Deep Reinforcement Learning
A. Haj-Ali, N. K. Ahmed, T. Willke, S. Shao, K. Asanovic, and I. Stoica · 2020
Closest in time.
Cost-Guided Cardinality Estimation: Focus Where it Matters
P. Negi, R. Marcus, H. Mao, N. Tatbul, T. Kraska, and M. Alizadeh · 2020
Closest in time.