Pyramid-based Scatterplots Sampling for Progressive and Streaming Data Visualization

We present a pyramid-based scatterplot sampling technique to avoid overplotting and enable progressive and streaming visualization of large data. Our technique is based on a multiresolution pyramid-based decomposition of the underlying density map and makes use of the density values in the pyramid t...

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Bibliographic Details
Published inIEEE transactions on visualization and computer graphics Vol. 28; no. 1; pp. 593 - 603
Main Authors Chen, Xin, Zhang, Jian, Fu, Chi-Wing, Fekete, Jean-Daniel, Wang, Yunhai
Format Journal Article
LanguageEnglish
Published United States IEEE 01.01.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Institute of Electrical and Electronics Engineers
Subjects
big data
Coherence
Computer Science
Data visualization
Density
Human-Computer Interaction
Outliers (statistics)
progressive visualization
pyramid
Reservoirs
Rivers
sampling
Sampling methods
Scalability
Scatterplots
Scientific visualization
streaming visualization
Visual analytics
Visualization
streaming visualization
Scatterplots
sampling
pyramid
progressive visualization
big data
scalability
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Summary:We present a pyramid-based scatterplot sampling technique to avoid overplotting and enable progressive and streaming visualization of large data. Our technique is based on a multiresolution pyramid-based decomposition of the underlying density map and makes use of the density values in the pyramid to guide the sampling at each scale for preserving the relative data densities and outliers. We show that our technique is competitive in quality with state-of-the-art methods and runs faster by about an order of magnitude. Also, we have adapted it to deliver progressive and streaming data visualization by processing the data in chunks and updating the scatterplot areas with visible changes in the density map. A quantitative evaluation shows that our approach generates stable and faithful progressive samples that are comparable to the state-of-the-art method in preserving relative densities and superior to it in keeping outliers and stability when switching frames. We present two case studies that demonstrate the effectiveness of our approach for exploring large data.
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ISSN:1077-2626
1941-0506
1941-0506
DOI:10.1109/TVCG.2021.3114880