Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/68378
Title: StreamMap : smooth dynamic visualization of high-density streaming points
Authors: Li, CH 
Baciu, G 
Han, Y
Keywords: Information visualization
Trend visualization
Streaming data
Density map
Time-varying
Scatterplots
Issue Date: 2016
Source: IEEE transactions on visualization and computer graphics, 2016, no. 99, p. 1-14 How to cite?
Journal: IEEE transactions on visualization and computer graphics 
Abstract: 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 heatmap. We propose a new approach, called StreamMap, 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 called super kernel density estimation that aggregates streaming points using an adaptive kernel to solve the overlapping problem. The second contribution is a robust density morphing algorithm that 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.
URI: http://hdl.handle.net/10397/68378
ISSN: 1077-2626
EISSN: 1941-0506
DOI: 10.1109/TVCG.2017.2668409
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