High Performance or Low Memory? An Updatable Learned Index Framework for Time-Space Tradeoff
Summary: Introduces LIFT, an updatable learned-index framework that derives theoretical time–space correlation models and minimizes a time-space cost function to navigate the performance vs. memory tradeoff. Adds structural adjustments to resist dense/duplicate inserts and poisoning attacks, yielding robust, consistently optimal time-space tradeoffs and outperforming prior learned and traditional indexes. (summarized by gpt-5-mini on Feb 11 2026)
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BibTeX Citation
@inproceedings{wang_sigmod26,
title = {{High Performance or Low Memory? An Updatable Learned Index Framework for Time-Space Tradeoff}},
author = {Wang, Hui and Wang, Xin and Ge, Jiake and Chai, Yunpeng and Liang, Lei and Yi, Peng},
series = {{SIGMOD} '26},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3769800},
url = {https://dl.acm.org/doi/10.1145/3769800},
year = {2026}
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