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Query optimization is one of the most challenging problems in database systems.
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CORDS: Automatic Discovery of Correlations and Soft Functional Dependencies
I. F. Ilyas, V. Markl, P. Haas, P. Brown, and A. Aboulnaga · 2004
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Towards a Robust Query Optimizer: A Principled and Practical Approach
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Inside the SQL Server Query Optimizer
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Deep Learning of Representations for Unsupervised and Transfer Learning
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Linguistic Regularities in Continuous Space Word Representations
T. Mikolov, W.-t. Yih, and G. Zweig · 2013
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Is Query Optimization a ‘”Solved” Problem?
G. Lohman · 2014
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Pre-training Neural Networks with Human Demonstrations for Deep Reinforcement Learning
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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
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A survey of deep neural network architectures and their applications
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Toward Multimodal Image-to-Image Translation
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The Vertica Query Optimizer: The case for specialized query optimizers
N. Tran, A. Lamb, L. Shrinivas, S. Bodagala, and J. Dave · 2014
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Retrofitting Word Vectors to Semantic Lexicons
M. Faruqui, J. Dodge, S. K. Jauhar, C. Dyer, E. H. Hovy, and N. A. Smith · 2015
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Adam: A Method for Stochastic Optimization
D. P. Kingma and J. Ba · 2015
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Deep learning
Y. LeCun, Y. Bengio, and G. Hinton · 2015
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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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Cuttlefish: A Lightweight Primitive for Adaptive Query Processing
T. Kaftan, M. Balazinska, A. Cheung, and J. Gehrke · 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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Learning scheduling algorithms for data processing clusters
H. Mao, M. Schwarzkopf, S. B. Venkatakrishnan, Z. Meng, and M. Alizadeh · 2018
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Innateness, AlphaZero, and Artificial Intelligence
G. Marcus · 2018
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Deep Reinforcement Learning for Join Order Enumeration
R. Marcus and O. Papaemmanouil · 2018
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Deep Learning for Entity Matching: A Design Space Exploration
S. Mudgal, H. Li, T. Rekatsinas, A. Doan, Y. Park, G. Krishnan, R. Deep, E. Arcaute, and V. Raghavendra · 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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QuickSel: Quick Selectivity Learning with Mixture Models
Y. Park, S. Zhong, and B. Mozafari · 2018
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Deep Contextualized Word Representations
M. Peters, M. Neumann, M. Iyyer, M. Gardner, C. Clark, K. Lee, and L. Zettlemoyer · 2018
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LIFT: Reinforcement Learning in Computer Systems by Learning From Demonstrations
M. Schaarschmidt, A. Kuhnle, B. Ellis, K. Fricke, F. Gessert, and E. Yoneki · 2018
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SkinnerDB: Regret-bounded Query Evaluation via Reinforcement Learning
I. Trummer, S. Moseley, D. Maram, S. Jo, and J. Antonakakis · 2018
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Refining Word Embeddings Using Intensity Scores for Sentiment Analysis
L. Yu, J. Wang, K. R. Lai, and X. Zhang · 2018
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Termite: A System for Tunneling Through Heterogeneous Data
R. C. Fernandez and S. Madden · 2019
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Learned Cardinalities: Estimating Correlated Joins with Deep Learning
A. Kipf, T. Kipf, B. Radke, V. Leis, P. Boncz, and A. Kemper · 2019
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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
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Towards a Hands-Free Query Optimizer through Deep Learning
R. Marcus and O. Papaemmanouil · 2019
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