Largest Triangle Sampling for Visualizing Time Series in Database

In time series visualization, sampling is used to reduce the number of points while retaining the visual features of the raw time series. Area-based Largest Triangle Sampling (LTS) excels at preserving perceptually critical points. However, the heuristic solution to LTS by sequentially sampling points with the locally largest triangle area (a.k.a. Largest-Triangle-Three-Buckets, LTTB) suffers from suboptimal solution and query inefficiency. We address the shortcomings by contributing a novel Iterative Largest Triangle Sampling (ILTS) algorithm with convex hull acceleration. It refines the sampling results iteratively, capturing a broader perspective by integrating more points in each iteration. Remarkably, we prove that the largest triangle can always be found in the precomputed convex hulls, making the iterative sampling still efficient. Experiments demonstrate increased visual quality over state-of-the-art baselines and significant speedups over the brute force approach.