Art Design Methods Based on Big Data Analysis

  • Dong ShaoEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1117)


With the progress of science and technology and the continuous development of social economy in the new era, art design has become an indispensable factor to promote economic development. The development of Internet technology provides favorable conditions for the expansion of the field of big data, and the collaboration of big data cloud computing enables the Internet to achieve efficient operation. Under the background of data, the combination of Internet and cloud computing technology can satisfy more functions, especially provide more powerful conditions for promoting the development of art design. In recent years, the research on big data analysis and art design methods has been deepening, which makes it easy for us to find that in the art design research based on big data analysis, we should pay attention to the characteristics of current information development, and combine digital information with network technology to improve work efficiency. By studying art design based on big data analysis, this paper analyzes the existing problems of art design under the background of big data, and puts forward solutions according to the existing problems, which provides better design ideas and methods for art design and facilitates the rapid development of art design.


Big data analysis Art design Method study Genetic coding algorithm 


  1. 1.
    Lo’ai, A.T., Mehmood, R., Benkhlifa, E.: Mobile cloud computing model and big data analysis for healthcare applications. IEEE Access 4(99), 6171–6180 (2017)Google Scholar
  2. 2.
    Bo, T., Zhen, C., Hefferman, G.: Incorporating intelligence in fog computing for big data analysis in smart cities. IEEE Trans. Ind. Inform. 13(5), 2140–2150 (2017)CrossRefGoogle Scholar
  3. 3.
    Kamilaris, A., Kartakoullis, A., Prenafeta-Boldú, F.X.: A review on the practice of big data analysis in agriculture. Comput. Electron. Agric. 143(5), 23–37 (2017)CrossRefGoogle Scholar
  4. 4.
    Sun, J., Jeliazkova, N., Chupakhin, V.: Erratum to: ExCAPE-DB: an integrated large scale dataset facilitating big data analysis in chemogenomics. J. Cheminformatics 9(1), 17 (2017)CrossRefGoogle Scholar
  5. 5.
    Kung, S.Y.: Discriminant component analysis for privacy protection and visualization of big data. Multimed. Tools Appl. 76(3), 3999–4034 (2017)CrossRefGoogle Scholar
  6. 6.
    Drovandi, C.C., Holmes, C., Mcgree, J.M.: Principles of experimental design for big data analysis. Stat. Sci. Rev. J. Inst. Math. Stat. 32(3), 385 (2017)MathSciNetzbMATHGoogle Scholar
  7. 7.
    Balint, T.S., Pangaro, P.: Design space for space design: dialogs through boundary objects at the intersections of art, design, science, and engineering. Acta Astronaut. 134(3), 41–53 (2017)CrossRefGoogle Scholar
  8. 8.
    Sugiura, S., Ishihara, T., Nakao, M.: State-of-the-art design of index modulation in the space, time, and frequency domains: benefits and fundamental limitations. IEEE Access 5(99), 21774–21790 (2017)CrossRefGoogle Scholar
  9. 9.
    Sun, J.: Studio teaching mode of art design subject of vocational college: a case study of “art workshop” teaching mode of Nanjing vocational institute of industry technology. J. Landsc. Res. 3, 118–120 (2017)Google Scholar
  10. 10.
    Bates, V.: ‘Humanizing’ healthcare environments: architecture, art and design in modern hospitals. Des. Health 2(1), 5–19 (2018)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Dalian Neusoft University of InformationDalianChina

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