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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
4.9793485e-05
Overall Rank
11,184 | 24.81%
DOI
10.1145/3725292

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Authors

BibTeX Citation

@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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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 Cited Paper Year Venue Pagerank
9 Online Aggregation 1997 SIGMOD 0.00076195956
71 DuckDB: an Embeddable Analytical Database 2019 SIGMOD 0.00037720227
135 Ripple Joins for Online Aggregation 1999 SIGMOD 0.00029866033
318 DeepDB: Learn from Data, not from Queries! 2020 VLDB 0.00021167555
406 Deep Unsupervised Cardinality Estimation 2020 VLDB 0.00019045544
564 Congressional Samples for Approximate Answering of Group-By Queries 2000 SIGMOD 0.00016296665
596 Wander Join: Online Aggregation via Random Walks 2016 SIGMOD 0.00015785583
784 VerdictDB: Universalizing Approximate Query Processing 2018 SIGMOD 0.00014012614
840 Quickr: Lazily Approximating Complex AdHoc Queries in BigData Clusters 2016 SIGMOD 0.0001354605
1,064 Are We Ready For Learned Cardinality Estimation? 2021 VLDB 0.00012202282
1,829 DBEst: Revisiting Approximate Query Processing Engines with Machine Learning Models 2019 SIGMOD 9.5510333e-05
2,000 Sample + Seek: Approximating Aggregates with Distribution Precision Guarantee 2016 SIGMOD 9.2112617e-05
2,004 FLAT: Fast, Lightweight and Accurate Method for Cardinality Estimation 2021 VLDB 9.2065719e-05
2,027 Database Learning: Toward a Database that Becomes Smarter Every Time 2017 SIGMOD 9.1618139e-05
3,741 FACE: A Normalizing Flow based Cardinality Estimator 2022 VLDB 7.0594076e-05
4,781 Learned Approximate Query Processing: Make it Light, Accurate and Fast 2021 CIDR 6.4162085e-05
5,657 PGMJoins: Random Join Sampling with Graphical Models 2021 SIGMOD 6.0488437e-05
6,004 Approximate Query Engines: Commercial Challenges and Research Opportunities 2017 SIGMOD 5.9166815e-05
6,221 Combining Aggregation and Sampling (Nearly) Optimally for Approximate Query Processing 2021 SIGMOD 5.8463347e-05
7,166 Learning to Sample: Counting with Complex Queries 2020 VLDB 5.5949741e-05
7,375 PairwiseHist: Fast, Accurate and Space-Efficient Approximate Query Processing with Data Compression 2024 VLDB 5.5400509e-05
8,331 LAQy: Efficient and Reusable Query Approximations via Lazy Sampling 2023 SIGMOD 5.3532622e-05
8,659 ShadowAQP: Efficient Approximate Group-by and Join Query via Attribute-oriented Sample Size Allocation and Data Generation 2023 VLDB 5.2930951e-05
8,770 One Size Does Not Fit All: A Bandit-Based Sampler Combination Framework with Theoretical Guarantees 2022 SIGMOD 5.2812395e-05
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