Tsunami: A Learned Multi-dimensional Index for Correlated Data and Skewed Workloads
Summary: Tsunami, a learned multi-dimensional index for correlated data and skewed workloads, addresses tuning gaps in prior learned MDIs. It delivers up to 6x faster queries and 8x smaller index than prior learned MDI, and up to 11x faster and 170x smaller than optimally-tuned traditional indexes. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Jialin Ding (Massachusetts Institute of Technology)
- 2. Vikram Nathan (Massachusetts Institute of Technology)
- 3. Mohammad Alizadeh (Massachusetts Institute of Technology)
- 4. Tim Kraska (Massachusetts Institute of Technology)
BibTeX Citation
@article{ding_vldb21,
title = {{Tsunami: A Learned Multi-dimensional Index for Correlated Data and Skewed Workloads}},
author = {Ding, Jialin and Nathan, Vikram and Alizadeh, Mohammad and Kraska, Tim},
journal = {PVLDB},
series = {{VLDB} '21},
volume = {14},
number = {2},
pages = {74--86},
doi = {10.14778/3425879.3425880},
url = {https://doi.org/10.14778/3425879.3425880},
year = {2021}
}Incoming Citations (Sorted by Pagerank)
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Outgoing Citations (Sorted by Pagerank)
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