Smooth dynamic visualization of high-density streaming points
Smooth dynamic visualization of high-density streaming points
复制标题
高密度流点的平滑动态可视化
DOI：
10.1109/tvcg.2017.2668409
复制
发表时间：
--
影响因子：
5.2
通讯作者：
Han Yu
中科院分区：
文献类型：
--
作者：
Li Chenhui;Baciu George;Han Yu
实时散点图的流点交互式可视化和相关模式的线性混合正日益成为大数据和来自主动传感器和广播媒体的流数据的视觉分析的主导模式。为了更好地可视化流间模式并与流间模式交互，通常需要平滑流数据中的间隙或失真。以前的方法要么直接对点进行动画处理，要么呈现采样的静态热图。我们提出了一种新的方法，calledStreamMap，平滑地混合高密度流点，并创建一个视觉流，强调密度模式分布。从本质上讲，我们提出了三个新的贡献，高密度流点的可视化。第一个贡献是一个基于密度的方法，称为超级核密度估计，聚合流点使用自适应内核来解决重叠问题。第二个贡献是一个鲁棒密度变形算法，生成几个光滑的中间帧为一个给定的帧对。第三个贡献是一个趋势表示设计，可以帮助传达流点的流向。三个数据集上的实验结果表明，StreamMap的有效性时，需要动态可视化和可视化分析的趋势模式的流点。
Interactive visualization of streaming points for real-time scatterplots and linear blending of correlation patterns is increasingly becoming the dominant mode of visual analytics for both big data and streaming data from active sensors and broadcasting media. To better visualize and interact with inter-stream patterns, it is generally necessary to smooth out gaps or distortions in the streaming data. Previous approaches either animate the points directly or present a sampled static heat-map. We propose a new approach, calledStreamMap, to smoothly blend high-density streaming points and create a visual flow that emphasizes the density pattern distributions. In essence, we present three new contributions for the visualization of high-density streaming points. The first contribution is a density-based method calledsuper kernel density estimationthat aggregates streaming points using an adaptive kernel to solve the overlapping problem. The second contribution is a robustdensity morphing algorithmthat generates several smooth intermediate frames for a given pair of frames. The third contribution is a trend representation design that can help convey the flow directions of the streaming points. The experimental results on three datasets demonstrate the effectiveness of StreamMap when dynamic visualization and visual analysis of trend patterns on streaming points are required.