FINEdex: A Fine-grained Learned Index Scheme for Scalable and Concurrent Memory Systems
Summary: FINEdex builds independent, fine-grained learned models on a flattened data layout to minimize data dependency and enable in-place, concurrent lookups. Non-blocking retraining avoids large delta-buffers, delivering up to 1.8x–2.5x speedups over XIndex and Masstree (open-source). (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Pengfei Li (Huazhong University of Science and Technology; Wuhan University)
- 2. Yu Hua (Huazhong University of Science and Technology; Wuhan University)
- 3. Jingnan Jia (Huazhong University of Science and Technology; Wuhan University)
- 4. Pengfei Zuo (Huazhong University of Science and Technology; Wuhan University)
BibTeX Citation
@article{li_vldb22,
title = {{FINEdex: A Fine-grained Learned Index Scheme for Scalable and Concurrent Memory Systems}},
author = {Li, Pengfei and Hua, Yu and Jia, Jingnan and Zuo, Pengfei},
journal = {PVLDB},
series = {{VLDB} '22},
volume = {15},
number = {2},
pages = {321--334},
doi = {10.14778/3489496.3489512},
url = {https://doi.org/10.14778/3489496.3489512},
year = {2022}
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