Towards Designing and Learning Piecewise Space-Filling Curves
Summary: Proposes piecewise space-filling curves that apply different mapping schemes per data subspace, enabling locally tailored locality-preserving mappings instead of a single global SFC. Introduces Bit Merging tree (BMTree) and an RL-based builder that jointly derives subspaces and their SFCs, improving multidimensional-to-1D indexing and query performance over existing SFCs. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Jiangneng Li (Nanyang Technological University)
- 2. Zheng Wang (Nanyang Technological University)
- 3. Gao Cong (Nanyang Technological University)
- 4. Cheng Long (Nanyang Technological University)
- 5. Han Mao Kiah (Nanyang Technological University)
- 6. Bin Cui (Peking University)
BibTeX Citation
@article{li_vldb23,
title = {{Towards Designing and Learning Piecewise Space-Filling Curves}},
author = {Li, Jiangneng and Wang, Zheng and Cong, Gao and Long, Cheng and Kiah, Han Mao and Cui, Bin},
journal = {PVLDB},
series = {{VLDB} '23},
volume = {16},
number = {9},
pages = {2158--2171},
doi = {10.14778/3598581.3598589},
url = {https://doi.org/10.14778/3598581.3598589},
year = {2023}
}
Incoming Citations (Sorted by Pagerank)
Showing 5 of 5 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,569 | Toward Drift-Aware Database Benchmarking | 2026 | VLDB | 5.093636e-05 |
| 10,670 | MAST: Towards Efficient Analytical Query Processing on Point Cloud Data | 2025 | SIGMOD | 5.093636e-05 |
| 10,672 | Optimizing Block Skipping for High-Dimensional Data with Learned Adaptive Curve | 2025 | SIGMOD | 5.093636e-05 |
| 11,193 | BT-Tree: A Reinforcement Learning Based Index for Big Trajectory Data | 2024 | SIGMOD | 5.093636e-05 |
| 11,342 | Efficient Cost Modeling of Space-filling Curves | 2024 | VLDB | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 19 of 19 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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