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In this paper, we present our vision of so called zero-shot learning for databases which is a new learning approach for database components.
An efficient cost-driven index selection tool for microsoft sql server
S. Chaudhuri and V. R. Narasayya · 1997
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Automated selection of materialized views and indexes in sql databases
S. Agrawal, S. Chaudhuri, and V. R. Narasayya · 2000
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Automatic physical database tuning: A relaxation-based approach
N. Bruno and S. Chaudhuri · 2005
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Continuous resource monitoring for self-predicting dbms
D. Narayanan, E. Thereska, and A. Ailamaki · 2005
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Automated partitioning design in parallel database systems
R. Nehme and N. Bruno · 2011
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Value iteration networks
A. Tamar, S. Levine, P. Abbeel, Y. Wu, and G. Thomas · 2016
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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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Model-free control for distributed stream data processing using deep reinforcement learning
T. Li, Z. Xu, J. Tang, and Y. Wang · 2018
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Deep reinforcement learning for join order enumeration
R. Marcus and O. Papaemmanouil · 2018
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Fiting-tree: A data-aware index structure
A. Galakatos, M. Markovitch, C. Binnig, R. Fonseca, and T. Kraska · 2019
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Learning data structure alchemy
S. Idreos, K. Zoumpatianos, S. Chatterjee, W. Qin, A. Wasay, B. Hentschel, M. S. Kester, N. Dayan, D. Guo, M. Kang, and Y. Sun · 2019
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Learned cardinalities: Estimating correlated joins with deep learning
A. Kipf, T. Kipf, B. Radke, V. Leis, P. A. Boncz, and A. Kemper · 2019
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Opportunistic view materialization with deep reinforcement learning
X. Liang, A. J. Elmore, and S. Krishnan · 2019
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Learning scheduling algorithms for data processing clusters
H. Mao, M. Schwarzkopf, S. B. Venkatakrishnan, Z. Meng, and M. Alizadeh · 2019
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Neo: A learned query optimizer
R. Marcus, P. Negi, H. Mao, C. Zhang, M. Alizadeh, T. Kraska, O. Papaemmanouil, and N. Tatbul · 2019
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Plan-structured deep neural network models for query performance prediction
Alex: An updatable adaptive learned index
J. Ding, U. F. Minhas, J. Yu, C. Wang, J. Do, Y. Li, H. Zhang, B. Chandramouli, J. Gehrke, D. Kossmann, D. Lomet, and T. Kraska · 2020
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Tsunami: A learned multi-dimensional index for correlated data and skewed workloads
J. Ding, V. Nathan, M. Alizadeh, and T. Kraska · 2020
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DBMS fitting: Why should we learn what we already know?
B. Hilprecht, C. Binnig, T. Bang, M. El-Hindi, B. Hättasch, A. Khanna, R. Rehrmann, U. Röhm, A. Schmidt, L. Thostrup, and T. Ziegler · 2020
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Learning a partitioning advisor for cloud databases
B. Hilprecht, C. Binnig, and U. Röhm · 2020
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Deepdb: Learn from data, not from queries!
B. Hilprecht, A. Schmidt, M. Kulessa, A. Molina, K. Kersting, and C. Binnig · 2020
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An index advisor using deep reinforcement learning
H. Lan, Z. Bao, and Y. Peng · 2020
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R. Marcus and O. Papaemmanouil · 2019
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Scheduling OLTP transactions via learned abort prediction
Y. Sheng, A. Tomasic, T. Zhang, and A. Pavlo · 2019
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An end-to-end learning-based cost estimator
J. Sun and G. Li · 2019
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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
Cited alongside, same era.
An end-to-end automatic cloud database tuning system using deep reinforcement learning
J. Zhang, Y. Liu, K. Zhou, G. Li, Z. Xiao, B. Cheng, J. Xing, Y. Wang, T. Cheng, L. Liu, M. Ran, and Z. Li · 2019
Cited alongside, same era.
Language models are few-shot learners
T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, and D. Amodei · 2020
Cited alongside, same era.
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Bao: Learning to steer query optimizers, 2020
R. Marcus, P. Negi, H. Mao, N. Tatbul, M. Alizadeh, and T. Kraska · 2020
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Neurocard: One cardinality estimator for all tables
Z. Yang, A. Kamsetty, S. Luan, E. Liang, Y. Duan, X. Chen, and I. Stoica · 2020
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High-dimensional bayesian optimization with multi-task learning for rocksdb
S. Alabed and E. Yoneki · 2021
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Towards a general framework for ml-based self-tuning databases
T. Schmied, D. Didona, A. Döring, T. Parnell, and N. Ioannou · 2021
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Predicting cpu usage for proactive autoscaling
T. Wang, S. Ferlin, and M. Chiesa · 2021
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