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Learning a Partitioning Advisor for Cloud Databases
Summary: DRL-based partitioning advisor for cloud DBMS automates partitioning decisions for OLAP workloads by learning cost tradeoffs and placement policies. Evaluations show it finds non-trivial partitionings across diverse workloads and outperforms classical automated approaches.
(summarized by gpt-5-nano on Feb 09 2026)
Paper ID
5981
Venue
SIGMOD
Year
2020
Pagerank
8.4993549e-05
Overall Rank
2,499 | 82.86%
DOI
10.1145/3318464.3389704
Incoming Non-self Citations Over Time
BibTeX Citation
@inproceedings{hilprecht_sigmod20,
title = {{Learning a Partitioning Advisor for Cloud Databases}},
author = {Hilprecht, Benjamin and Binnig, Carsten and Röhm, Uwe},
series = {{SIGMOD} '20},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3318464.3389704},
url = {https://dl.acm.org/doi/10.1145/3318464.3389704},
year = {2020}
}
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Showing 19 of 19 cited papers.
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Neo: A Learned Query Optimizer 2019
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DB2 Design Advisor: Integrated Automatic Physical Database Design 2004
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An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning 2019
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SageDB: A Learned Database System 2019
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Automated Partitioning Design in Parallel Database Systems 2011
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