LM-Tree: A Hybrid Learned Index for Similarity Search in Metric Spaces
Summary: Hybrid learned M-Tree for metric-space similarity search: each node adaptively chooses pivots and a lightweight model to boost pruning, avoiding the weak single-pivot vs costly multi-pivot tradeoff. Efficient maintenance supports updates via pivot/model re-optimization, splits, and merges with low overhead. (summarized by gpt-5-mini on Apr 11 2026)
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Authors
- 1. Yaqi Wang (Northeastern University)
- 2. Bin Wang (Northeastern University)
- 3. Rui Zhu (Shenyang Aerospace University)
- 4. Wenli Sun (Northeastern University)
- 5. Xiaochun Yang (Northeastern University)
BibTeX Citation
@inproceedings{wang_sigmod26,
title = {{LM-Tree: A Hybrid Learned Index for Similarity Search in Metric Spaces}},
author = {Wang, Yaqi and Wang, Bin and Zhu, Rui and Sun, Wenli and Yang, Xiaochun},
series = {{SIGMOD} '26},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3786665},
url = {https://dl.acm.org/doi/10.1145/3786665},
year = {2026}
}
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