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Learning Multi-dimensional Indexes
Summary: Flood is a learnable, in-memory multi-dimensional index that jointly optimizes structure and data layout to adapt to a dataset and workload. It delivers up to three orders of magnitude faster range scans than state-of-the-art alternatives, a building block for end-to-end learned DB systems.
(summarized by gpt-5-nano on Feb 09 2026)
Paper ID
h72ba21dacba08e3e
Venue
SIGMOD
Year
2020
Pagerank
0.00013354403
Overall Rank
868 | 94.17%
DOI
10.1145/3318464.3380579
Incoming Non-self Citations Over Time
BibTeX Citation
Copy BibTeX
@inproceedings{nathan_sigmod20,
title = {{Learning Multi-dimensional Indexes}},
author = {Nathan, Vikram and Ding, Jialin and Alizadeh, Mohammad and Kraska, Tim},
series = {{SIGMOD} '20},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3318464.3380579},
url = {https://dl.acm.org/doi/10.1145/3318464.3380579},
year = {2020}
}
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