Approximate Query Processing under Updates
Summary: Allowing small approximation yields an AQP algorithm that maintains any free-connex aggregation query in O(log n) amortized time per insertion-only update. Works in a general semiring (count/sum/avg/max/distinct), gives large practical speedups with 10% error, and logarithmic bounds for fully dynamic updates ruled out by lower bounds. (summarized by gpt-5-mini on Feb 11 2026)
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Authors
- 1. Binyang Dai (Hong Kong University of Science and Technology)
- 2. Ke Yi (Hong Kong University of Science and Technology)
BibTeX Citation
@inproceedings{dai_sigmod26,
title = {{Approximate Query Processing under Updates}},
author = {Dai, Binyang and Yi, Ke},
series = {{SIGMOD} '26},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3769760},
url = {https://dl.acm.org/doi/10.1145/3769760},
year = {2026}
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