Updatable Learned Indexes Meet Disk-Resident DBMS - From Evaluations to Design Choices
Summary: Disk-resident updatable learned indexes vs B+-tree; four SOTA methods implemented. Findings: B+-tree robust on disk; learned indexes beat it only on select workloads; design principles: lower height, lean structures, faster scans, compact storage. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Hai Lan (Royal Melbourne Institute of Technology)
- 2. Zhifeng Bao (Royal Melbourne Institute of Technology)
- 3. J. Shane Culpepper (Royal Melbourne Institute of Technology)
- 4. Renata Borovica-Gajic (University of Melbourne)
BibTeX Citation
@inproceedings{lan_sigmod23,
title = {{Updatable Learned Indexes Meet Disk-Resident DBMS - From Evaluations to Design Choices}},
author = {Lan, Hai and Bao, Zhifeng and Culpepper, J. Shane and Borovica-Gajic, Renata},
series = {{SIGMOD} '23},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3589284},
url = {https://dl.acm.org/doi/10.1145/3589284},
year = {2023}
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