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LeCo: Lightweight Compression via Learning Serial Correlations
Summary: LeCo uses learned models to remove serial redundancy in columnar data, unifying FOR, Delta, and RLE as a single framework. Real and synthetic data show Pareto gains in compression and random access; Arrow yields up to 5.2x speedups, RocksDB 16% throughput gain.
(summarized by gpt-5-nano on Feb 09 2026)
Paper ID
hc45d9419287cdc04
Venue
SIGMOD
Year
2024
Pagerank
6.7526687e-05
Overall Rank
4,169 | 72.06%
DOI
10.1145/3639320
Incoming Non-self Citations Over Time
BibTeX Citation
Copy BibTeX
@inproceedings{liu_sigmod24,
title = {{LeCo: Lightweight Compression via Learning Serial Correlations}},
author = {Liu, Yihao and Zeng, Xinyu and Zhang, Huanchen},
series = {{SIGMOD} '24},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3639320},
url = {https://dl.acm.org/doi/10.1145/3639320},
year = {2024}
}
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