DBScholar

Back to papers

NFL: Robust Learned Index via Distribution Transformation

Summary: NFL: a two-stage learned index that first applies Numerical Normalizing Flow to transform skewed key distributions into near-uniform, then builds the index on transformed keys. Introduces After-Flow Learned Index (AFLI) for robustness, with experiments showing higher throughput and lower tail latency than state-of-the-art learned indexes on synthetic and real workloads. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
12902
Venue
VLDB
Year
2022
Pagerank
6.7159984e-05
Overall Rank
4,414 | 69.72%
DOI
10.14778/3547305.3547322

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{wu_vldb22,
        title = {{NFL: Robust Learned Index via Distribution Transformation}},
        author = {Wu, Shangyu and Cui, Yufei and Yu, Jinghuan and Sun, Xuan and Kuo, Tei-Wei and Xue, Chun Jason},
        journal = {PVLDB},
        series = {{VLDB} '22},
        volume = {15},
        number = {10},
        pages = {2188--2200},
        doi = {10.14778/3547305.3547322},
        url = {https://doi.org/10.14778/3547305.3547322},
        year = {2022}
}

Incoming Citations (Sorted by Pagerank)

Showing 14 of 14 citing papers.

Previous Page 1 / 1 Next

Outgoing Citations (Sorted by Pagerank)

Showing 14 of 14 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Previous Page 1 / 1 Next

Semantically Similar Papers