Can Learned Indexes be Built Efficiently? A Deep Dive into Sampling Trade-offs
Summary: Build-time bottleneck in learned indexes tackled via sampled learning: Sample EB-PLA and Sample EB-Histogram, both error-bounded. Shows >10x faster construction while exposing new trade-offs among sampling rate, model error, size, and lookup latency. (summarized by gpt-5.4-mini on May 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Minguk Choi (Dankook University)
- 2. Seehwan Yoo (Dankook University)
- 3. Jongmoo Choi (Dankook University)
BibTeX Citation
@inproceedings{choi_sigmod24,
title = {{Can Learned Indexes be Built Efficiently? A Deep Dive into Sampling Trade-offs}},
author = {Choi, Minguk and Yoo, Seehwan and Choi, Jongmoo},
series = {{SIGMOD} '24},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3654919},
url = {https://dl.acm.org/doi/10.1145/3654919},
year = {2024}
}Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 9,599 | Understanding Robustness Issues of Updatable Learned Indexes: [Experiments & Analysis] | 2026 | SIGMOD | 5.2492748e-05 |
| 10,458 | From Learning to Recycling: A Log-Structured Learned-Less Index | 2026 | SIGMOD | 5.093636e-05 |
| 10,461 | HIRE: A Hybrid Learned Index for Robust and Efficient Performance under Mixed Workloads | 2026 | SIGMOD | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 21 of 21 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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