Accelerating Approximate Aggregation Queries with Expensive Predicates
Summary: ABae accelerates approximate aggregates with selective, DNN-based predicates by stratifying records using cheap proxies, despite proxy samples violating predicates. Pilot sampling and plugin estimates achieve optimal allocation and convergence, reducing labeling costs up to 2.3×. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. Daniel Kang (Stanford University)
- 2. John Guibas (Stanford University)
- 3. Peter Bailis (Stanford University)
- 4. Tatsunori Hashimoto (Stanford University)
- 5. Yi Sun (University of Chicago)
- 6. Matei Zaharia (Stanford University)
BibTeX Citation
@article{kang_vldb21,
title = {{Accelerating Approximate Aggregation Queries with Expensive Predicates}},
author = {Kang, Daniel and Guibas, John and Bailis, Peter and Hashimoto, Tatsunori and Sun, Yi and Zaharia, Matei},
journal = {PVLDB},
series = {{VLDB} '21},
volume = {14},
number = {11},
pages = {2341--2354},
doi = {10.14778/3476249.3476285},
url = {https://doi.org/10.14778/3476249.3476285},
year = {2021}
}
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