A Universal Sketch for Estimating Heavy Hitters and Per-Element Frequency Moments in Data Streams with Bounded Deletions
Summary: Introduces Removable Augmented Sketch and Removable Universal Sketch for bounded-deletion turnstile streams, enabling online, memory-efficient estimation of heavy hitters, per-element frequencies, frequency moments, and distribution. Augments with Online Moment Estimator and compressed counters to boost accuracy and memory, delivering 16–69% F1 gains and up to 3e4× throughput in moment estimation. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Liang Zheng (Southeast University)
- 2. Qingjun Xiao (Purple Mountain Laboratories; Southeast University)
- 3. Xuyuan Cai (Hong Kong Polytechnic University)
BibTeX Citation
@inproceedings{zheng_sigmod24,
title = {{A Universal Sketch for Estimating Heavy Hitters and Per-Element Frequency Moments in Data Streams with Bounded Deletions}},
author = {Zheng, Liang and Xiao, Qingjun and Cai, Xuyuan},
series = {{SIGMOD} '24},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3698799},
url = {https://dl.acm.org/doi/10.1145/3698799},
year = {2024}
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