Largest Triangle Sampling for Visualizing Time Series in Database
Summary: Proposes Iterative Largest Triangle Sampling (ILTS) with convex-hull acceleration for time-series visualization. It iteratively refines samples; uses precomputed hulls to guarantee largest triangle, yielding higher fidelity and speedups over brute force. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Lei Rui (Tsinghua University)
- 2. Xiangdong Huang (Tsinghua University)
- 3. Shaoxu Song (Tsinghua University)
- 4. Chen Wang (Tsinghua University)
- 5. Jianmin Wang (Tsinghua University)
- 6. Zhao Cao (Huawei)
BibTeX Citation
@inproceedings{rui_sigmod25,
title = {{Largest Triangle Sampling for Visualizing Time Series in Database}},
author = {Rui, Lei and Huang, Xiangdong and Song, Shaoxu and Wang, Chen and Wang, Jianmin and Cao, Zhao},
series = {{SIGMOD} '25},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3709699},
url = {https://dl.acm.org/doi/10.1145/3709699},
year = {2025}
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Outgoing Citations (Sorted by Pagerank)
Showing 5 of 5 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,500 | M4: A Visualization-Oriented Time Series Data Aggregation | 2014 | VLDB | 0.00010460169 |
| 3,623 | Apache IoTDB: A Time Series Database for IoT Applications | 2023 | SIGMOD | 7.150278e-05 |
| 4,595 | Sim-Piece: Highly Accurate Piecewise Linear Approximation through Similar Segment Merging | 2023 | VLDB | 6.5097157e-05 |
| 6,136 | OM3: An Ordered Multi-level Min-Max Representation for Interactive Progressive Visualization of Time Series | 2023 | SIGMOD | 5.8738296e-05 |
| 6,319 | Visualization-aware Time Series Min-Max Caching with Error Bound Guarantees | 2024 | VLDB | 5.8133264e-05 |
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