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Carefully selected materialized views can greatly improve the performance of OLAP workloads.
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Recommending materialized views and indexes with the ibm db2 design advisor
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Dynamic materialization of query views for data warehouse workloads
Opportunistic physical design for big data analytics
J. LeFevre, J. Sankaranarayanan, H. Hacigumus, J. Tatemura, N. Polyzotis, and M. J. Carey · 2014
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History-aware query optimization with materialized intermediate views
L. L. Perez and C. M. Jermaine · 2014
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Don’t throw out your algorithms book just yet: Classical data structures that can outperform learned indexes
P. Bailis, K. S. Tai, P. Thaker, and M. Zaharia · 2017
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Learned cardinalities: Estimating correlated joins with deep learning
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The case for learned index structures
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T. Phan and W.-S. Li · 2008
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Double q-learning
H. V. Hasselt · 2010
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Generating databases for query workloads
E. Lo, N. Cheng, and W.-K. Hon · 2010
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A survey of view selection methods
I. Mami and Z. Bellahsene · 2012
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Recycling in pipelined query evaluation
F. Nagel, P. Boncz, and S. D. Viglas · 2013
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Recycling intermediate results in pipelined query evaluation
F. Nagel
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S. Krishnan, Z. Yang, K. Goldberg, J. Hellerstein, and I. Stoica · 2018
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Query-based workload forecasting for self-driving database management systems
L. Ma, D. Van Aken, A. Hefny, G. Mezerhane, A. Pavlo, and G. J. Gordon · 2018
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Deep reinforcement learning for join order enumeration
R. Marcus and O. Papaemmanouil · 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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Reinforcement learning: An introduction
R. S. Sutton and A. G. Barto · 2018
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