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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
7283
Venue
SIGMOD
Year
2025
Pagerank
5.093636e-05
Overall Rank
10,760 | 26.18%
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 0.00077458002
103 DuckDB: an Embeddable Analytical Database 2019 SIGMOD 0.00034161428
131 Ripple Joins for Online Aggregation 1999 SIGMOD 0.00030424509
323 DeepDB: Learn from Data, not from Queries! 2020 VLDB 0.00021264788
401 Deep Unsupervised Cardinality Estimation 2020 VLDB 0.00019092557
553 Congressional Samples for Approximate Answering of Group-By Queries 2000 SIGMOD 0.00016590619
593 Wander Join: Online Aggregation via Random Walks 2016 SIGMOD 0.00016027871
772 VerdictDB: Universalizing Approximate Query Processing 2018 SIGMOD 0.00014147905
819 Quickr: Lazily Approximating Complex AdHoc Queries in BigData Clusters 2016 SIGMOD 0.00013815639
1,061 Are We Ready For Learned Cardinality Estimation? 2021 VLDB 0.00012369764
1,799 DBEst: Revisiting Approximate Query Processing Engines with Machine Learning Models 2019 SIGMOD 9.7326398e-05
1,962 Sample + Seek: Approximating Aggregates with Distribution Precision Guarantee 2016 SIGMOD 9.3978414e-05
1,988 FLAT: Fast, Lightweight and Accurate Method for Cardinality Estimation 2021 VLDB 9.3501502e-05
1,995 Database Learning: Toward a Database that Becomes Smarter Every Time 2017 SIGMOD 9.3403665e-05
3,688 FACE: A Normalizing Flow based Cardinality Estimator 2022 VLDB 7.201795e-05
4,789 Learned Approximate Query Processing: Make it Light, Accurate and Fast 2021 CIDR 6.5072039e-05
5,551 PGMJoins: Random Join Sampling with Graphical Models 2021 SIGMOD 6.1782856e-05
5,906 Approximate Query Engines: Commercial Challenges and Research Opportunities 2017 SIGMOD 6.0457047e-05
6,206 Combining Aggregation and Sampling (Nearly) Optimally for Approximate Query Processing 2021 SIGMOD 5.9443409e-05
7,048 Learning to Sample: Counting with Complex Queries 2020 VLDB 5.7178054e-05
7,351 PairwiseHist: Fast, Accurate and Space-Efficient Approximate Query Processing with Data Compression 2024 VLDB 5.6354898e-05
8,161 LAQy: Efficient and Reusable Query Approximations via Lazy Sampling 2023 SIGMOD 5.4752972e-05
8,492 ShadowAQP: Efficient Approximate Group-by and Join Query via Attribute-oriented Sample Size Allocation and Data Generation 2023 VLDB 5.4145838e-05
8,608 One Size Does Not Fit All: A Bandit-Based Sampler Combination Framework with Theoretical Guarantees 2022 SIGMOD 5.4024561e-05
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