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DBEst: Revisiting Approximate Query Processing Engines with Machine Learning Models
Summary: DBEst revisits AQP with ML regression models and density estimators to deliver fast, accurate analytical approximations. It targets low memory, broad aggregate support, and integration with existing systems; TPC-DS and real data show gains over state-of-the-art AQP engines.
(summarized by gpt-5-nano on Feb 09 2026)
Paper ID
h694ccd533ed12028
Venue
SIGMOD
Year
2019
Pagerank
9.5510333e-05
Overall Rank
1,829 | 87.71%
DOI
10.1145/3299869.3324958
Incoming Non-self Citations Over Time
BibTeX Citation
Copy BibTeX
@inproceedings{ma_sigmod19,
title = {{DBEst: Revisiting Approximate Query Processing Engines with Machine Learning Models}},
author = {Ma, Qingzhi and Triantafillou, Peter},
series = {{SIGMOD} '19},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3299869.3324958},
url = {https://dl.acm.org/doi/10.1145/3299869.3324958},
year = {2019}
}
Incoming Citations (Sorted by Pagerank)
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318
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Outgoing Citations (Sorted by Pagerank)
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Spark SQL: Relational Data Processing in Spark
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2015
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2018
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2,232
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3,087
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2010
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