Advertisement

Cluster Computing

, Volume 22, Supplement 2, pp 2731–2738 | Cite as

A new cluster computing technique for social media data analysis

  • Qingzhen XuEmail author
  • Miao Li
Article

Abstract

Social media data analysis has emerged as an important part of the new generation of information technology. However, Social media data analysis is based on Data Mining. A new algorithm based on \(\hbox {M}^{\mathrm{x}}/\hbox {G}/1\) Queue model is proposed to calculate money flow of Social media stocks. The proposed algorithm exploits the transaction data behind the social media stocks, such as volume, the Commission transaction queue, closing price, etc. Through the Social media stock trading data, we use Matlab programming to carry out data mining and calculation of money flows, and draw the money inflow and outflow curve. The experimental results show that our new model has strong practicability for the data mining of Social media stocks. The experimental results can also reflect the development of the social media more clearly. The method of data mining on the Social media stocks provides an indirect way to study the economic and regional economy of the Social media stocks.

Keywords

Social media \(\hbox {M}^{\mathrm{x}}/\hbox {G}/1\) queue Data mining Money flow Computing technique 

Notes

Acknowledgements

The Project was supported by the National Natural Science Foundation of China (No. 61402185), Natural Science Foundation of Guangdong Province (No. 2015A030313382), and Guangdong Provincial Public Research and Capacity Building Foundation funded project (Nos. 2016A020223012, 2015A020217011).

References

  1. 1.
    Yao, H., Xiong, M., et al.: Mining multiple spatial–temporal paths from social media data. Future Gener. Comput. Syst.  https://doi.org/10.1016/j.future.2017.08.003 (2017)
  2. 2.
    Liu, S., Young, S.D.: A survey of social media data analysis for physical activity surveillance. J. Forensic Legal Med.  https://doi.org/10.1016/j.jflm.2016.10.019 (2017)
  3. 3.
    Blazquez, D., Domenech, J.: Big Data sources and methods for social and economic analyses. Technol. Forecast. Soc. Change.  https://doi.org/10.1016/j.techfore.2017.07.027 (2017)
  4. 4.
    Injadat, M.N., Salo, F., Nassif, A.B.: Data mining techniques in social media: a survey. Neurocomputing 214, 654–670 (2016)CrossRefGoogle Scholar
  5. 5.
    Shao, H., Zhang, Y., Li, W.: Extraction and analysis of city’s tourism districts based on social media data. Comput. Environ. Urban Syst. 65(9), 66–78 (2017)CrossRefGoogle Scholar
  6. 6.
    Brandt, T., Bendler, J., Neumann, D.: Social media analytics and value creation in urban smart tourism ecosystems. Inf. Manag. 54, 703–713 (2017)CrossRefGoogle Scholar
  7. 7.
    Cui, W., Wang, P.: An algorithm for event detection based on social media data. Neurocomputing 254, 53–58 (2017)CrossRefGoogle Scholar
  8. 8.
    Singh, A., Shukl, N., et al.: Social media data analytics to improve supply chain management in food industries. Transp. Res. E. (2017)Google Scholar
  9. 9.
    Qiang, W., Zhi, J., Yan, X.: Service discovery for internet of things based on probabilistic topic model. J. Softw. 25(8), 1640–1658 (2014)Google Scholar
  10. 10.
    Zhihong, Q., Yiju, W.: loT Technology and Application. ACTA Electron. Sin. 40(5), 1023–1029 (2012)Google Scholar
  11. 11.
    Yunquan, G., Xiaoyong, L., Binxing, F.: Survey on the search of internet of things. J. Commun. 36(12), 57–76 (2015)Google Scholar
  12. 12.
    Haiming, C., Li, C., Kaibin, X.: A comparative study on architectures and implementation methodologies of internet of things. Chin. J. Comput. 36(1), 168–188 (2013)Google Scholar
  13. 13.
    Haiming, C., Li, C., Kaibin, X.: Information sensing and interaction technology in internet of things. Chin. J. Comput. 35(6), 1147–1163 (2012)CrossRefGoogle Scholar
  14. 14.
    Qinyan, M., Shubin, S.: Information model and capability analysis of internet of things. J. Softw. 25(8), 1685–1695 (2014)Google Scholar
  15. 15.
    l-Fuqaha, A., Guizani, M., et al.: Internet of things: a survey on enabling technologies, protocols and applications. IEEE Commun. Surv. Tutor.  https://doi.org/10.1109/COMST.2015.2444095 (2015)
  16. 16.
    Nan, J., Liang, Y., et al.: A novel exercise thermophysiology comfort prediction model with fuzzy logic. Mobile Inf. Syst.  https://doi.org/10.1155/2016/8586493 (2016)
  17. 17.
    Qingzhen, X., Susu, B., et al.: \(\text{ M }^{\text{ x }}/\text{ G }/1\) queue with multiple vacations. Stoch. Anal. Appl. 25(1), 127–140 (2007)Google Scholar
  18. 18.
    Bo, C., Wen-Sheng, C.: Noisy image segmentation based on wavelet transform and active contour model. Appl. Anal. 90(8), 1243–1255 (2011)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Bo, C., Qing-Hua, Z., et al.: A novel adaptive partial differential equation model for image segmentation. Appl. Anal. 93(11), 2440–2450 (2012)zbMATHGoogle Scholar
  20. 20.
    Xiuli, L., Zengqin, Z.: Iterative technique for a third-order differential equation with three-point nonlinear boundary value conditions. Electron. J. Qual. Theory Differ. Equ. 12(1), 1–10 (2016).  https://doi.org/10.14232/ejqtde.2016.1.12
  21. 21.
    Xiuli, L., Zengqin, Z.: Sign-changing solution for a third-order boundary-value problem in ordered Banach space with lattice structure. Bound. Value Probl.  https://doi.org/10.1186/1687-2770-2014-132 (2014)
  22. 22.
    Qingzhen, X., Zhoutao, W., et al.: Thermal comfort research on human CT data modeling. Multimed. Tools Appl.  https://doi.org/10.1007/s11042-017-4537-9 (2017)
  23. 23.
    Xiuli, L., Zengqin, Z.: Existence and uniqueness of symmetric positive solutions of 2n-order nonlinear singular boundary value problems. Appl. Math. Lett. 26, 92–698 (2013)MathSciNetzbMATHGoogle Scholar
  24. 24.
    Peihe, W., Lingling, Z.: Some geometrical properties of convex level sets of minimal graph on 2-dimensional Riemannian manifolds. Nonlinear Anal. Theory Method Appl. 130(1), 1–13 (2016)MathSciNetzbMATHGoogle Scholar
  25. 25.
    Peihe, W., Dekai, Z.: Convexity of level sets of minimal graph on space form with nonnegative curvature. J. Differ. Equ. 262, 5534–5564 (2017)MathSciNetCrossRefGoogle Scholar
  26. 26.
    Fushan, L., Qingyong, G.: Blow-up of solution for a nonlinear Petrovsky type equation with memory. Appl. Math. Comput. 274, 383–392 (2016)MathSciNetzbMATHGoogle Scholar
  27. 27.
    Li, G., Zhang, Z., Wang, L., Pan, J., Chen, Q.: One-class collaborative filtering based on rating prediction and ranking prediction. Knowl. Based Syst. 124, 46–54 (2017)CrossRefGoogle Scholar
  28. 28.
    Li, G., Weihua, O.: Pairwise probabilistic matrix factorization for implicit feedback collaborative filtering. Neurocomputing 204, 17–25 (2016)CrossRefGoogle Scholar
  29. 29.
    Li, G., Wang, L., Li, Y.: Robust personalized ranking from implicit feedback. Int. J. Pattern Recognit. Artif. Intell. 30(1), 1659001:1-28 (2016)Google Scholar
  30. 30.
    Li, G., Chen, Q.: Exploiting explicit and implicit feedback for personalized ranking. Math. Probl. Eng. 2016, 1–11 (2016)MathSciNetzbMATHGoogle Scholar
  31. 31.
    Yang, J., Li, J., Liu, S.: A novel technique applied to the economic investigation of recommender system. Multimed. Tools Appl. 8, 1–16 (2017)Google Scholar
  32. 32.
    Xu, Q., Wu, J., Chen, Q.: A novel mobile personalized recommended method based on money flow model for stock exchange. Math. Probl. Eng. 2014, 353910 (2014)MathSciNetzbMATHGoogle Scholar
  33. 33.
    Xu, Q.: A novel machine learning strategy based on two-dimensional numerical models in financial engineering. Math. Probl. Eng. 2, 1–6 (2013)Google Scholar
  34. 34.
    Ma, Z.: Te Geom/G/1 queue with multiple vacation and server set-up/close times. Oper. Res. Manag. Sci. 13(1), 21–25 (2004)Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2017

Authors and Affiliations

  1. 1.School of ComputerSouth China Normal UniversityGuangzhouChina

Personalised recommendations