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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
7205
Venue
SIGMOD
Year
2025
Pagerank
4.1905499e-05
Overall Rank
10,481 | 27.16%
DOI
10.1145/3725266

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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
8 Optimal Aggregation Algorithms for Middleware [Extended Abstract] 2001 PODS 0.0015436578
72 Combining Fuzzy Information from Multiple Systems 1996 PODS 0.00058498396
763 A Comprehensive Survey and Experimental Comparison of Graph-Based Approximate Nearest Neighbor Search 2021 VLDB 0.00016963981
1,805 Top-k Query Evaluation with Probabilistic Guarantees 2004 VLDB 0.00010479371
1,884 Tuplex: Data Science in Python at Native Code Speed 2021 SIGMOD 0.00010206514
2,904 Evaluating End-to-End Optimization for Data Analytics Applications in Weld 2018 VLDB 7.9403097e-05
3,639 The Design of an LLM-powered Unstructured Analytics System 2025 CIDR 6.8886648e-05
3,649 One WITH RECURSIVE is Worth Many GOTOs 2021 SIGMOD 6.8764882e-05
4,813 Putting Pandas in a Box 2021 CIDR 5.8993009e-05
4,909 A Method for Optimizing Opaque Filter Queries 2020 SIGMOD 5.8354189e-05
5,386 Selective Data Acquisition in the Wild for Model Charging 2022 VLDB 5.5346315e-05
5,741 Babelfish: Efficient Execution of Polyglot Queries 2022 VLDB 5.3450701e-05
8,162 MONSOON: Multi-Step Optimization and Execution of Queries with Partially Obscured Predicates 2020 SIGMOD 4.5686903e-05
8,365 NeurDB: On the Design and Implementation of an AI-powered Autonomous Database 2025 CIDR 4.5305127e-05
8,580 Efficient Execution of User-Defined Functions in SQL Queries 2023 VLDB 4.4876382e-05
8,646 Optimizing Video Selection LIMIT Queries With Commonsense Knowledge 2024 VLDB 4.4720866e-05
8,651 ApproxML: Efficient Approximate Ad-Hoc ML Models Through Materialization and Reuse 2019 VLDB 4.4710004e-05
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