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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
h8c9c52a8fd741011
Venue
SIGMOD
Year
2025
Pagerank
4.9793485e-05
Overall Rank
11,177 | 24.86%
DOI
10.1145/3725266

Incoming Non-self Citations Over Time

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Authors

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}
}

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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 0.0010679641
172 Combining Fuzzy Information from Multiple Systems 1996 PODS 0.00026835705
345 A Comprehensive Survey and Experimental Comparison of Graph-Based Approximate Nearest Neighbor Search 2021 VLDB 0.00020445545
1,535 Top-k Query Evaluation with Probabilistic Guarantees 2004 VLDB 0.00010331242
1,803 Tuplex: Data Science in Python at Native Code Speed 2021 SIGMOD 9.6068397e-05
2,249 Evaluating End-to-End Optimization for Data Analytics Applications in Weld 2018 VLDB 8.7549752e-05
2,341 The Design of an LLM-powered Unstructured Analytics System 2025 CIDR 8.6066183e-05
3,194 One WITH RECURSIVE is Worth Many GOTOs 2021 SIGMOD 7.5498012e-05
3,384 Putting Pandas in a Box 2021 CIDR 7.3550055e-05
4,293 A Method for Optimizing Opaque Filter Queries 2020 SIGMOD 6.6819917e-05
4,867 Selective Data Acquisition in the Wild for Model Charging 2022 VLDB 6.3749346e-05
5,230 Babelfish: Efficient Execution of Polyglot Queries 2022 VLDB 6.2189001e-05
7,983 Efficient Execution of User-Defined Functions in SQL Queries 2023 VLDB 5.4131297e-05
8,345 MONSOON: Multi-Step Optimization and Execution of Queries with Partially Obscured Predicates 2020 SIGMOD 5.3511996e-05
8,385 NeurDB: On the Design and Implementation of an AI-powered Autonomous Database 2025 CIDR 5.3420959e-05
8,749 Optimizing Video Selection LIMIT Queries With Commonsense Knowledge 2024 VLDB 5.2852539e-05
8,807 ApproxML: Efficient Approximate Ad-Hoc ML Models Through Materialization and Reuse 2019 VLDB 5.2728127e-05
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