Approximating Opaque Top-k Queries

Summary: Approximates opaque top-k queries with a task-agnostic hierarchical index and a bandit. Histogram-based modeling targets fat tails and yields a constant-factor approximation, with up to 10× speedups over exhaustive scan. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 7266
Venue: SIGMOD
Year: 2025
Pagerank: 5.093636e-05
Overall Rank: 10,751 | 26.24%
DOI: 10.1145/3725266

Incoming Non-self Citations Over Time

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Authors

1. Jiwon Chang (University of Rochester)
2. Fatemeh Nargesian (University of Rochester)

BibTeX Citation

@inproceedings{chang_sigmod25,
        title = {{Approximating Opaque Top-k Queries}},
        author = {Chang, Jiwon and Nargesian, Fatemeh},
        series = {{SIGMOD} '25},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3725266},
        url = {https://dl.acm.org/doi/10.1145/3725266},
        year = {2025}
}

Incoming Citations (Sorted by Pagerank)

Showing 0 of 0 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank

Outgoing Citations (Sorted by Pagerank)

Showing 17 of 17 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
5	Optimal Aggregation Algorithms for Middleware [Extended Abstract]	2001	PODS	0.0010828372
170	Combining Fuzzy Information from Multiple Systems	1996	PODS	0.00027376361
398	A Comprehensive Survey and Experimental Comparison of Graph-Based Approximate Nearest Neighbor Search	2021	VLDB	0.00019194947
1,519	Top-k Query Evaluation with Probabilistic Guarantees	2004	VLDB	0.00010513777
1,768	Tuplex: Data Science in Python at Native Code Speed	2021	SIGMOD	9.8041636e-05
2,316	Evaluating End-to-End Optimization for Data Analytics Applications in Weld	2018	VLDB	8.7596739e-05
2,956	The Design of an LLM-powered Unstructured Analytics System	2025	CIDR	7.9203461e-05
3,257	One WITH RECURSIVE is Worth Many GOTOs	2021	SIGMOD	7.590651e-05
4,264	A Method for Optimizing Opaque Filter Queries	2020	SIGMOD	6.7937529e-05
4,292	Putting Pandas in a Box	2021	CIDR	6.7784325e-05
4,808	Selective Data Acquisition in the Wild for Model Charging	2022	VLDB	6.4991553e-05
5,301	Babelfish: Efficient Execution of Polyglot Queries	2022	VLDB	6.2750553e-05
8,096	Efficient Execution of User-Defined Functions in SQL Queries	2023	VLDB	5.4875738e-05
8,211	MONSOON: Multi-Step Optimization and Execution of Queries with Partially Obscured Predicates	2020	SIGMOD	5.4658735e-05
8,221	NeurDB: On the Design and Implementation of an AI-powered Autonomous Database	2025	CIDR	5.4640314e-05
8,589	Optimizing Video Selection LIMIT Queries With Commonsense Knowledge	2024	VLDB	5.4065627e-05
8,648	ApproxML: Efficient Approximate Ad-Hoc ML Models Through Materialization and Reuse	2019	VLDB	5.3920944e-05

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