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)
Incoming Non-self Citations Over Time
No non-self incoming citations found for this paper in this database.
Authors
- 1. Lei Rui
- 2. Xiangdong Huang
- 3. Shaoxu Song
- 4. Chen Wang
- 5. Jianmin Wang
- 6. Zhao Cao
Incoming Citations (Sorted by Pagerank)
Showing 0 of 0 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|
Previous
Page 1 / 1
Next
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 |
|---|---|---|---|---|
| 1,805 | M4: A Visualization-Oriented Time Series Data Aggregation | 2014 | VLDB | 0.00010493299 |
| 3,967 | Apache IoTDB: A Time Series Database for IoT Applications | 2023 | SIGMOD | 6.5796647e-05 |
| 4,628 | Sim-Piece: Highly Accurate Piecewise Linear Approximation through Similar Segment Merging | 2023 | VLDB | 6.0379315e-05 |
| 6,069 | OM3: An Ordered Multi-level Min-Max Representation for Interactive Progressive Visualization of Time Series | 2023 | SIGMOD | 5.2280784e-05 |
| 6,296 | Visualization-aware Time Series Min-Max Caching with Error Bound Guarantees | 2024 | VLDB | 5.1249171e-05 |
Previous
Page 1 / 1
Next