One Size Does Not Fit All: A Bandit-Based Sampler Combination Framework with Theoretical Guarantees

Summary: SamComb, a bandit-based sampler-assembly framework, blends uniform, stratified, and measure-biased samplers under a budget to estimate population parameters. It casts sampler selection as a multi-armed bandit with exploration–exploitation guarantees, delivering accuracy gains on synthetic and real data without assuming distribution. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 6414
Venue: SIGMOD
Year: 2022
Pagerank: 5.4024561e-05
Overall Rank: 8,608 | 40.95%
DOI: 10.1145/3514221.3517900

Incoming Non-self Citations Over Time

Authors

1. Jinglin Peng (Simon Fraser University)
2. Bolin Ding (Alibaba)
3. Jiannan Wang (Simon Fraser University)
4. Kai Zeng (Alibaba)
5. Jingren Zhou (Alibaba)

BibTeX Citation

@inproceedings{peng_sigmod22,
        title = {{One Size Does Not Fit All: A Bandit-Based Sampler Combination Framework with Theoretical Guarantees}},
        author = {Peng, Jinglin and Ding, Bolin and Wang, Jiannan and Zeng, Kai and Zhou, Jingren},
        series = {{SIGMOD} '22},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3514221.3517900},
        url = {https://dl.acm.org/doi/10.1145/3514221.3517900},
        year = {2022}
}

Incoming Citations (Sorted by Pagerank)

Showing 3 of 3 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
10,760	FAAQP: Fast and Accurate Approximate Query Processing based on Bitmap-augmented Sum-Product Network	2025	SIGMOD	5.093636e-05
10,806	AdaNDV: Adaptive Number of Distinct Value Estimation via Learning to Select and Fuse Estimators	2025	VLDB	5.093636e-05
11,194	Enabling Adaptive Sampling for Intra-Window Join: Simultaneously Optimizing Quantity and Quality	2024	SIGMOD	5.093636e-05

Outgoing Citations (Sorted by Pagerank)

Showing 22 of 22 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
378	Bao: Making Learned Query Optimization Practical	2021	SIGMOD	0.00019638121
553	Congressional Samples for Approximate Answering of Group-By Queries	2000	SIGMOD	0.00016590619
737	Join Size Estimation Subject to Filter Conditions	2015	VLDB	0.00014490983
772	VerdictDB: Universalizing Approximate Query Processing	2018	SIGMOD	0.00014147905
802	Random Sampling over Joins Revisited	2018	SIGMOD	0.00013907725
819	Quickr: Lazily Approximating Complex AdHoc Queries in BigData Clusters	2016	SIGMOD	0.00013815639
909	Dynamic Sample Selection for Approximate Query Processing	2003	SIGMOD	0.00013291205
1,009	Online Aggregation for Large MapReduce Jobs	2011	VLDB	0.00012684342
1,392	Northstar: An Interactive Data Science System	2018	VLDB	0.00010936065
1,401	Knowing When You’re Wrong: Building Fast and Reliable Approximate Query Processing Systems	2014	SIGMOD	0.00010889902
1,664	Two-Level Sampling for Join Size Estimation	2017	SIGMOD	0.00010070362
1,872	The Analytical Bootstrap: a New Method for Fast Error Estimation in Approximate Query Processing	2014	SIGMOD	9.5759874e-05
1,962	Sample + Seek: Approximating Aggregates with Distribution Precision Guarantee	2016	SIGMOD	9.3978414e-05
1,995	Database Learning: Toward a Database that Becomes Smarter Every Time	2017	SIGMOD	9.3403665e-05
2,312	Online Aggregation and Continuous Query support in MapReduce	2010	SIGMOD	8.7642158e-05
2,404	Cardinality Estimation Using Sample Views with Quality Assurance	2007	SIGMOD	8.6225576e-05
3,366	AQP++: Connecting Approximate Query Processing With Aggregate Precomputation for Interactive Analytics	2018	SIGMOD	7.4748604e-05
3,370	Revisiting Reuse for Approximate Query Processing	2017	VLDB	7.4700891e-05
5,743	Joins on Samples: A Theoretical Guide for Practitioners	2020	VLDB	6.1025457e-05
5,906	Approximate Query Engines: Commercial Challenges and Research Opportunities	2017	SIGMOD	6.0457047e-05
8,108	Experiences with Approximating Queries in Microsoft’s Production Big-Data Clusters	2019	VLDB	5.4850569e-05

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