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LITS: An Optimized Learned Index for Strings

Summary: LITS targets variable-length, skewed strings with a hash-enhanced prefix table plus per-node linear models and compact hybrid subtries, reducing learned-index last-mile costs. It outperforms HOT/ART by up to 2.43×/2.27× on point lookups while matching scan performance. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
13740
Venue
VLDB
Year
2024
Pagerank
5.6675134e-05
Overall Rank
7,224 | 50.44%
DOI
10.14778/3681954.3682010

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BibTeX Citation

@article{yang_vldb24,
        title = {{LITS: An Optimized Learned Index for Strings}},
        author = {Yang, Yifan and Chen, Shimin},
        journal = {PVLDB},
        series = {{VLDB} '24},
        volume = {17},
        number = {11},
        pages = {3415--3427},
        doi = {10.14778/3681954.3682010},
        url = {https://doi.org/10.14778/3681954.3682010},
        year = {2024}
}

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