VEGA: An Active-tuning Learned Index with Group-Wise Learning Granularity
Summary: VEGA uses active-tuning with group-wise granularity to simplify distribution and tighten lookup bounds. A memory-budget framework merges key grouping with online key repositioning to achieve strong theory and empirical lookup/build performance. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Meng Li (Nanjing University)
- 2. Huayi Chai (Nanjing University)
- 3. Siqiang Luo (Nanyang Technological University)
- 4. Haipeng Dai (Nanjing University)
- 5. Rong Gu (Nanjing University)
- 6. Jiaqi Zheng (Nanjing University)
- 7. Guihai Chen (Nanjing University)
BibTeX Citation
@inproceedings{li_sigmod25,
title = {{VEGA: An Active-tuning Learned Index with Group-Wise Learning Granularity}},
author = {Li, Meng and Chai, Huayi and Luo, Siqiang and Dai, Haipeng and Gu, Rong and Zheng, Jiaqi and Chen, Guihai},
series = {{SIGMOD} '25},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3709736},
url = {https://dl.acm.org/doi/10.1145/3709736},
year = {2025}
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| Rank | Citing Paper | Year | Venue | Pagerank |
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| 9,463 | Hourglass: An Adaptive Range Filter with Lightweight Hybrid Encoding | 2026 | SIGMOD | 5.2634238e-05 |
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