From Learning to Recycling: A Log-Structured Learned-Less Index
Summary: Learned-Less Index: a log-structured learned index for LSM KV stores that recycles/merges existing linear regression models instead of retraining from sorted keys, making model construction up to 615x faster than Greedy-PLR. Prebuilt models are available at SSTable creation, improving query speed and compaction under high concurrency despite larger prediction error. (summarized by gpt-5-mini on Apr 11 2026)
Incoming Non-self Citations Over Time
No non-self incoming citations found for this paper in this database.
Authors
- 1. Hera Koo (Sungkyunkwan University)
- 2. Sungho Moon (Sungkyunkwan University)
- 3. Sangeun Chae (Sungkyunkwan University)
- 4. Wook-Hee Kim (Konkuk University)
- 5. Jongmoo Choi (Dankook University)
- 6. Beomseok Nam (Sungkyunkwan University)
BibTeX Citation
@inproceedings{koo_sigmod26,
title = {{From Learning to Recycling: A Log-Structured Learned-Less Index}},
author = {Koo, Hera and Moon, Sungho and Chae, Sangeun and Kim, Wook-Hee and Choi, Jongmoo and Nam, Beomseok},
series = {{SIGMOD} '26},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3786654},
url = {https://dl.acm.org/doi/10.1145/3786654},
year = {2026}
}Incoming Citations (Sorted by Pagerank)
Showing 0 of 0 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
Showing 8 of 8 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 43 | The Case for Learned Index Structures | 2018 | SIGMOD | 0.00046060254 |
| 447 | ALEX: An Updatable Adaptive Learned Index | 2020 | SIGMOD | 0.00018322593 |
| 477 | The PGM-index: a fully-dynamic compressed learned index with provable worst-case bounds | 2020 | VLDB | 0.00017851226 |
| 790 | FITing-Tree: A Data-aware Index Structure | 2019 | SIGMOD | 0.0001401445 |
| 847 | Benchmarking Learned Indexes | 2021 | VLDB | 0.0001365768 |
| 1,551 | Updatable Learned Index with Precise Positions | 2021 | VLDB | 0.00010381398 |
| 1,580 | Compaction management in distributed key-value datastores | 2015 | VLDB | 0.00010299919 |
| 9,411 | Can Learned Indexes be Built Efficiently? A Deep Dive into Sampling Trade-offs | 2024 | SIGMOD | 5.274743e-05 |
Previous
Page 1 / 1
Next
Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 4,660 | Hist-Tree: Those Who Ignore It Are Doomed to Learn | 2021 | CIDR |
| 2 | 447 | ALEX: An Updatable Adaptive Learned Index | 2020 | SIGMOD |
| 3 | 8,214 | Tuning Hierarchical Learned Indexes on Disk and Beyond | 2022 | SIGMOD |
| 4 | 10,277 | On Self-Designing Learned Indexes | 2026 | SIGMOD |
| 5 | 10,262 | LINE: A Learned Index with Group-Enhanced Leaves and Cache-Optimized Inner Tree | 2026 | SIGMOD |
| 6 | 2,806 | Are Updatable Learned Indexes Ready? | 2022 | VLDB |
| 7 | 7,368 | Accelerating String-key Learned Index Structures via Memoization-based Incremental Training | 2024 | VLDB |
| 8 | 6,687 | Making In-Memory Learned Indexes Efficient on Disk | 2024 | SIGMOD |
| 9 | 847 | Benchmarking Learned Indexes | 2021 | VLDB |
| 10 | 3,792 | Learned Index: A Comprehensive Experimental Evaluation | 2023 | VLDB |