LeaFi: Data Series Indexes on Steroids with Learned Filters
Summary: LeaFi uses learned filters to boost pruning in tree-based data-series indexes. Models predict tight node-wise distance lower bounds for pruning, with train-time index building and query-time calibration to meet per-query recall targets; up to 20x pruning, 32x search speed at 99% recall.— (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Qitong Wang (Université Paris Cité)
- 2. Ioana Ileana (Université Paris Cité)
- 3. Themis Palpanas (French University Institute; Université Paris Cité)
BibTeX Citation
@inproceedings{wang_sigmod25,
title = {{LeaFi: Data Series Indexes on Steroids with Learned Filters}},
author = {Wang, Qitong and Ileana, Ioana and Palpanas, Themis},
series = {{SIGMOD} '25},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3709701},
url = {https://dl.acm.org/doi/10.1145/3709701},
year = {2025}
}
Incoming Citations (Sorted by Pagerank)
Showing 4 of 4 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 7,145 | Subspace Collision: An Efficient and Accurate Framework for High-dimensional Approximate Nearest Neighbor Search | 2025 | SIGMOD | 5.6908957e-05 |
| 9,360 | DARTH: Declarative Recall Through Early Termination for Approximate Nearest Neighbor Search | 2026 | SIGMOD | 5.2819088e-05 |
| 10,297 | TaCo: Data-adaptive and Query-aware Subspace Collision for High-dimensional Approximate Nearest Neighbor Search | 2026 | SIGMOD | 5.093636e-05 |
| 10,430 | Honeybee: Efficient Role-based Access Control for Vector Databases via Dynamic Partitioning | 2026 | SIGMOD | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 30 of 30 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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