Advertisement

Cluster Computing

, Volume 22, Supplement 6, pp 13693–13702 | Cite as

E-commerce big data computing platform system based on distributed computing logistics information

  • Junmin HuEmail author
Article

Abstract

E-commerce websites generate a large amount of user behavior data, with the continuous increase of the business volume of e-commerce companies. Enterprises hope to have a deep understanding of each customer through these data and expect to form a learning relationship with customers. Based on this, this paper firstly elaborates the background and significance of the system development, the status quo of related technologies at home and abroad, then carries on the overall design and gives the system realization plan, and finally designs and realizes the big data T station based on distributed real-time computing framework, Enterprises, especially Internet companies, can provide internal data support by building a hierarchical data warehouse infrastructure and real-time data computing technology, and then cluster, divide and predict more relevant data through mathematical statistics and mining algorithms. Each of us is tagged with descriptions to get each of our attributes and hobbies, etc., providing a strong support for the external needs of enterprises and fast data-driven business.

Keywords

Distributed computing E-commerce Big data Computing platform 

References

  1. 1.
    Huang, T.E., Guo, Q., Sun, H.A.: Distributed computing platform supporting power system security knowledge discovery based on online simulation. IEEE Trans. Smart Grid. 8(99), 1–1 (2016)Google Scholar
  2. 2.
    Wu, P.L., He, F., Zhong, Y.: An envelope analysis algorithm of experiment data based on distributed computing platform. Cyberspace Secur. 1(2), 12–14 (2017)Google Scholar
  3. 3.
    Guo, W., Liu, F.: Research on parallel algorithm based on Hadoop distributed computing platform. Int. J. Grid Distrib. Comput. 8(1), 77–78 (2015)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Zhang, X., Jiang, J., Zhang, X., et al.: A data transmission algorithm for distributed computing system based on maximum flow. Cluster Comput. 18(3), 1157–1169 (2015)CrossRefGoogle Scholar
  5. 5.
    Rho, S., Park, S., Hwang, S.: Privacy enhanced data security mechanism in a large-scale distributed computing system for HTC and MTC. Int. J. Contents. 12(2), 6–11 (2016)CrossRefGoogle Scholar
  6. 6.
    Kumar, R.S., Chandrasekharan, E.: A parallel distributed computing framework for Newton-Raphson load flow analysis of large interconnected power systems. Int. J. Electr. Power Energy Syst. 73, 1–6 (2015)CrossRefGoogle Scholar
  7. 7.
    Hofmann, M., Rünger, G.: Sustainability through flexibility: building complex simulation programs for distributed computing systems. Simul. Model. Pract. Theory. 58, 65–78 (2015)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Party School of Guangxi District Organs of C. P. CNanningChina

Personalised recommendations