LARGE: A Length-Aggregation-based Grid Structure for Line Density Visualization
Summary: Introduces LARGE, a length-aggregation grid index and the first ε-relative-error approximation framework for Line Density Visualization, enabling scalable high-resolution LDV on very large line/trajectory datasets. Uses square and arbitrary-shaped lower/upper bound functions to prune computation; theoretical tightness and experiments show up to 291.8× speedups over exact methods. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Tsz Nam Chan (Shenzhen University)
- 2. Bojian Zhu (Hong Kong Baptist University)
- 3. Dingming Wu (Shenzhen University)
- 4. Yun Peng (Guangdong University of Technology)
- 5. Leong Hou U (University of Macau)
BibTeX Citation
@article{chan_vldb24,
title = {{LARGE: A Length-Aggregation-based Grid Structure for Line Density Visualization}},
author = {Chan, Tsz Nam and Zhu, Bojian and Wu, Dingming and Peng, Yun and U, Leong Hou},
journal = {PVLDB},
series = {{VLDB} '24},
volume = {17},
number = {13},
pages = {4585--4598},
doi = {10.14778/3704965.3704968},
url = {https://doi.org/10.14778/3704965.3704968},
year = {2024}
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| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 13,667 | A Fast Line Density Visualization Plugin for Geographic Information Systems | 2025 | SIGMOD | - |
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