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FAAQP: Fast and Accurate Approximate Query Processing based on Bitmap-augmented Sum-Product Network
Summary: FAAQP proposes a bitmap-augmented sum-product network (BSPN) for AQP, outperforming model- and sample-based methods. Budget-aware BSPN construction and bitmap merging enable tunable accuracy–latency trade-offs with 1.3x–9x gains and low latency.
(summarized by gpt-5-nano on Feb 09 2026)
Paper ID
h1663b4fcbf30df83
Venue
SIGMOD
Year
2025
Pagerank
4.9605343e-05
Overall Rank
11,252 | 24.58%
DOI
10.1145/3725292
Incoming Non-self Citations Over Time
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BibTeX Citation
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@inproceedings{zhang_sigmod25,
title = {{FAAQP: Fast and Accurate Approximate Query Processing based on Bitmap-augmented Sum-Product Network}},
author = {Zhang, Hanbing and Jing, Yinan and He, Zhenying and Zhang, Kai and Wang, X. Sean},
series = {{SIGMOD} '25},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3725292},
url = {https://dl.acm.org/doi/10.1145/3725292},
year = {2025}
}
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Citing Paper
Year
Venue
Pagerank
Outgoing Citations (Sorted by Pagerank)
Showing 24 of 24 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
Rank
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Year
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Pagerank
9
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1997
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0.00076079812
71
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0.00037688945
135
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1999
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0.00029805026
320
DeepDB: Learn from Data, not from Queries!
2020
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0.00021128745
405
Deep Unsupervised Cardinality Estimation
2020
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0.00019020138
564
Congressional Samples for Approximate Answering of Group-By Queries
2000
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0.00016251304
594
Wander Join: Online Aggregation via Random Walks
2016
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0.00015814493
770
VerdictDB: Universalizing Approximate Query Processing
2018
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0.00014086699
841
Quickr: Lazily Approximating Complex AdHoc Queries in BigData Clusters
2016
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0.00013503124
1,064
Are We Ready For Learned Cardinality Estimation?
2021
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0.00012181459
1,839
DBEst: Revisiting Approximate Query Processing Engines with Machine Learning Models
2019
SIGMOD
9.5187688e-05
2,009
FLAT: Fast, Lightweight and Accurate Method for Cardinality Estimation
2021
VLDB
9.1813272e-05
2,014
Sample + Seek: Approximating Aggregates with Distribution Precision Guarantee
2016
SIGMOD
9.1775869e-05
2,040
Database Learning: Toward a Database that Becomes Smarter Every Time
2017
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3,740
FACE: A Normalizing Flow based Cardinality Estimator
2022
VLDB
7.0398782e-05
4,792
Learned Approximate Query Processing: Make it Light, Accurate and Fast
2021
CIDR
6.4010108e-05
5,671
PGMJoins: Random Join Sampling with Graphical Models
2021
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6.026372e-05
6,014
Approximate Query Engines: Commercial Challenges and Research Opportunities
2017
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5.8956963e-05
6,198
Combining Aggregation and Sampling (Nearly) Optimally for Approximate Query Processing
2021
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5.8362492e-05
7,175
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5.5779755e-05
7,365
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5.5273291e-05
8,349
LAQy: Efficient and Reusable Query Approximations via Lazy Sampling
2023
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8,685
ShadowAQP: Efficient Approximate Group-by and Join Query via Attribute-oriented Sample Size Allocation and Data Generation
2023
VLDB
5.2730954e-05
8,790
One Size Does Not Fit All: A Bandit-Based Sampler Combination Framework with Theoretical Guarantees
2022
SIGMOD
5.2633515e-05
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