On Self-Designing Learned Indexes
Summary: SELIX replaces fixed learned-index heuristics with a unified, compositional template spanning node layouts, conflict policies, and search strategies. Deep RL with meta-learning performs online workload-to-structure optimization, adapting rapidly to drift and outperforming prior learned indexes in memory and on disk. (summarized by gpt-5.6-luna on Jul 26 2026)
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Authors
- 1. Baofu Han (Southeast University)
- 2. Guoyu Hu (National University of Singapore)
- 3. Bing Li (Southeast University)
- 4. Xiaokui Xiao (National University of Singapore)
- 5. Zhanhao Zhao (National University of Singapore)
- 6. Beng Chin Ooi (Zhejiang University)
BibTeX Citation
@inproceedings{han_sigmod26,
title = {{On Self-Designing Learned Indexes}},
author = {Han, Baofu and Hu, Guoyu and Li, Bing and Xiao, Xiaokui and Zhao, Zhanhao and Ooi, Beng Chin},
series = {{SIGMOD} '26},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3802096},
url = {https://dl.acm.org/doi/10.1145/3802096},
year = {2026}
}
Incoming Citations (Sorted by Pagerank)
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| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,272 | NeurBench: A Benchmark Suite for Learned Database Components with Drift Modeling: [Experiments & Analysis] | 2026 | SIGMOD | 5.093636e-05 |
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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