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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
9.5187688e-05
Overall Rank
1,839 | 87.68%
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}
}
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320
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2020
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Seiden: Revisiting Query Processing in Video Database Systems
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FAAQP: Fast and Accurate Approximate Query Processing based on Bitmap-augmented Sum-Product Network
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VLDB
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PECJ: Stream Window Join on Disorder Data Streams with Proactive Error Compensation
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Outgoing Citations (Sorted by Pagerank)
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Pagerank
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Spark SQL: Relational Data Processing in Spark
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CIDR
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SIGMOD
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2006
VLDB
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Learning Linear Regression Models over Factorized Joins
2016
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2015
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770
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2018
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Querying Continuous Functions in a Database System
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2,244
Shark: Fast Data Analysis Using Coarse-grained Distributed Memory
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Continuous Sampling for Online Aggregation Over Multiple Queries
2010
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2014
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Apache Tez: A Unifying Framework for Modeling and Building Data Processing Applications
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2015
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2017
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5.9218052e-05
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