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Survey on Visualization Layout for Big Data

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Intelligence Science and Big Data Engineering. Big Data and Machine Learning Techniques (IScIDE 2015)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9243))

Abstract

A reasonable visualization is helpful for the representation and analysis of big data, and an optimal layout improves the effect of visualization and provides a nice and direct platform for visualization analysis. In this paper surveys visualization literatures focusing on graph topology structure analysis or optimal layout, especially on those for big data visualization. Typical algorithms are classified into four categories, namely parallel coordinates, scatter diagram, tree map and other layout. We also propose the feasible directions for future research.

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Acknowledgment

This research work is supported by National Natural Science Foundation of China with projects Nos. 61372190, 61370193, and 61331018.

Author information

Authors and Affiliations

  1. College of Science, Beijing Forestry University, Beijing, 100083, China

    Pengju Teng & Hongjun Li

  2. NLPR-LIAMA, Institute of Automation, CAS, Beijing, 100190, China

    Xiaopeng Zhang

Authors
  1. Pengju Teng
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  2. Hongjun Li
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  3. Xiaopeng Zhang
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Corresponding author

Correspondence to Hongjun Li .

Editor information

Editors and Affiliations

  1. Zhejiang University, Hangzhou, China

    Xiaofei He

  2. Xidian University, Xi'an, China

    Xinbo Gao

  3. Northwestern Polytechnical University, Xi'an, China

    Yanning Zhang

  4. Nanjing University, Nanjing, China

    Zhi-Hua Zhou

  5. Chinese Academy of Sciences, Beijing, China

    Zhi-Yong Liu

  6. Suzhou University of Science and Technology, Suzhou, China

    Baochuan Fu

  7. Suzhou University of Science and Technology, Jiangsu, China

    Fuyuan Hu

  8. Suzhou University of Science and Technology, Jiangsu, China

    Zhancheng Zhang

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Teng, P., Li, H., Zhang, X. (2015). Survey on Visualization Layout for Big Data. In: He, X., et al. Intelligence Science and Big Data Engineering. Big Data and Machine Learning Techniques. IScIDE 2015. Lecture Notes in Computer Science(), vol 9243. Springer, Cham. https://doi.org/10.1007/978-3-319-23862-3_38

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  • DOI: https://doi.org/10.1007/978-3-319-23862-3_38

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23861-6

  • Online ISBN: 978-3-319-23862-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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