Tuning Hierarchical Learned Indexes on Disk and Beyond
Summary: Explores learned hierarchical indexes under external memory (disk/remote storage), where fast random access assumptions fail and I/O dominates cost. Shows that loading the entire index is impractical and per-key lookups trigger extra round-trips; proposes external-memory aware tuning that exploits on-disk key-position patterns to reduce costly accesses. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Supawit Chockchowwat (University of Illinois Urbana-Champaign)
BibTeX Citation
@inproceedings{chockchowwat_sigmod22,
title = {{Tuning Hierarchical Learned Indexes on Disk and Beyond}},
author = {Chockchowwat, Supawit},
series = {{SIGMOD} '22},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3514221.3520255},
url = {https://dl.acm.org/doi/10.1145/3514221.3520255},
year = {2022}
}Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 4,366 | DILI: A Distribution-Driven Learned Index | 2023 | VLDB | 6.6330579e-05 |
| 10,486 | LINE: A Learned Index with Group-Enhanced Leaves and Cache-Optimized Inner Tree | 2026 | SIGMOD | 4.9769913e-05 |
| 10,659 | HIRE: A Hybrid Learned Index for Robust and Efficient Performance under Mixed Workloads | 2026 | SIGMOD | 4.9769913e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 5 of 5 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 40 | The Case for Learned Index Structures | 2018 | SIGMOD | 0.00046363107 |
| 422 | ALEX: An Updatable Adaptive Learned Index | 2020 | SIGMOD | 0.00018488849 |
| 458 | The PGM-index: a fully-dynamic compressed learned index with provable worst-case bounds | 2020 | VLDB | 0.00017880664 |
| 768 | FITing-Tree: A Data-aware Index Structure | 2019 | SIGMOD | 0.00014107655 |
| 835 | Benchmarking Learned Indexes | 2021 | VLDB | 0.00013575971 |
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