A Backend-Agnostic Compiler for Approximate Query Processing with Probabilistic Tensor Algebra

Summary: BayesAQP compiles Bayesian-network inference for AQP into optimized tensor contractions, incorporating join histograms and avoiding per-group evaluation. Its backend-agnostic BTL++ execution targets CPUs/GPUs, delivering 37× speedups on complex multiway joins and group-bys at comparable accuracy. (summarized by gpt-5.6-luna on Jul 26 2026)

Paper ID: 7376
Venue: SIGMOD
Year: 2026
Pagerank: 5.093636e-05
Overall Rank: 10,185 | 30.13%
DOI: 10.1145/3802003

Incoming Non-self Citations Over Time

No non-self incoming citations found for this paper in this database.

Authors

1. Jingwen Pan (The University of Edinburgh)
2. James Cheney (The University of Edinburgh)
3. Amir Shaikhha (The University of Edinburgh)

BibTeX Citation

@inproceedings{pan_sigmod26,
        title = {{A Backend-Agnostic Compiler for Approximate Query Processing with Probabilistic Tensor Algebra}},
        author = {Pan, Jingwen and Cheney, James and Shaikhha, Amir},
        series = {{SIGMOD} '26},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3802003},
        url = {https://dl.acm.org/doi/10.1145/3802003},
        year = {2026}
}

Incoming Citations (Sorted by Pagerank)

Showing 0 of 0 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank

Outgoing Citations (Sorted by Pagerank)

Showing 13 of 13 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
18	How Good Are Query Optimizers, Really?	2016	VLDB	0.00059284255
323	DeepDB: Learn from Data, not from Queries!	2020	VLDB	0.00021264788
593	Wander Join: Online Aggregation via Random Walks	2016	SIGMOD	0.00016027871
772	VerdictDB: Universalizing Approximate Query Processing	2018	SIGMOD	0.00014147905
1,767	IDEBench: A Benchmark for Interactive Data Exploration	2020	SIGMOD	9.805856e-05
1,799	DBEst: Revisiting Approximate Query Processing Engines with Machine Learning Models	2019	SIGMOD	9.7326398e-05
2,991	FactorJoin: A New Cardinality Estimation Framework for Join Queries	2023	SIGMOD	7.8880723e-05
4,789	Learned Approximate Query Processing: Make it Light, Accurate and Fast	2021	CIDR	6.5072039e-05
5,414	Optimizing Tensor Programs on Flexible Storage	2023	SIGMOD	6.2258658e-05
7,351	PairwiseHist: Fast, Accurate and Space-Efficient Approximate Query Processing with Data Compression	2024	VLDB	5.6354898e-05
8,204	PilotDB: Database-Agnostic Online Approximate Query Processing with A Priori Error Guarantees	2025	SIGMOD	5.4667903e-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
9,392	A Step Toward Deep Online Aggregation	2023	SIGMOD	5.2755515e-05

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