Skip to main content
SpringerLink
Log in

Algorithm Analysis for Big Data in Education Based on Depth Learning

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

The construction of campus network has provided an advanced comprehensive information environment for teaching, scientific research and management of colleges. In the process of digitization and intelligentization, the data produced by all kinds of application systems in college are growing, and the large data environment of campus has been formed. Big data of college contain abundant information, so we need to use new data storage and analysis tools to store and analyze huge amounts of college data and get useful information from them. In this paper, a depth learning analysis algorithm based on Map Reduce is proposed to deal with college data. Using Map Reduce parallel computing framework to achieve campus data computing, we studied the analysis and application systems of campus big data in different themes and levels and dug out valuable information hidden behind college data. The experimental results show that the high school data mining algorithm based on Map Reduce is effective. It provides new research ideas for large data mining in colleges and provides technical reference for the construction of smart campus.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Liu, F., Shen, C., Lin, G., et al. (2016). Learning depth from single monocular images using deep convolutional neural fields. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(10), 2024.

    Article  Google Scholar 

  2. Baleia, J., Santana, P., & Barata, J. (2015). On exploiting haptic cues for self-supervised learning of depth-based robot navigation affordances. Journal of Intelligent and Robotic Systems, 80(3), 1–20.

    Google Scholar 

  3. Lazer, D., Kennedy, R., King, G., et al. (2014). Big data. The parable of Google Flu: Traps in big data analysis. Science, 343(6176), 1203.

    Article  Google Scholar 

  4. Fan, J., Han, F., & Liu, H. (2014). Challenges of big data analysis. National Science Review, 1(2), 293–314.

    Article  Google Scholar 

  5. Sandryhaila, A., & Moura, J. M. F. (2014). Big data analysis with signal processing on graphs: Representation and processing of massive data sets with irregular structure. IEEE Signal Processing Magazine, 31(5), 80–90.

    Article  Google Scholar 

  6. Shim, K. (2013). MapReduce algorithms for big data analysis. Proceedings of the VLDB Endowment, 5(12), 2016–2017.

    Article  Google Scholar 

  7. Alyass, A., Turcotte, M., & Meyre, D. (2015). From big data analysis to personalized medicine for all: challenges and opportunities. BMC Medical Genomics, 8(1), 33.

    Article  Google Scholar 

  8. Zhang, Y., Chen, M., Mao, S., et al. (2014). CAP: Community activity prediction based on big data analysis. Network IEEE, 28(4), 52–57.

    Article  Google Scholar 

  9. Mohammed, E. A., Far, B. H., & Naugler, C. (2014). Applications of the MapReduce programming framework to clinical big data analysis: current landscape and future trends. BioData Mining, 7(1), 22.

    Article  Google Scholar 

  10. Yoo, C., Ramirez, L., & Liuzzi, J. (2014). Big data analysis using modern statistical and machine learning methods in medicine. International Neurourology Journal, 18(2), 50.

    Article  Google Scholar 

  11. Park, J., Baek, Y. M., & Cha, M. (2014). Cross-cultural comparison of nonverbal cues in emoticons on Twitter: Evidence from big data analysis. Journal of Communication, 64(2), 333–354.

    Article  Google Scholar 

  12. Song, T. M., Song, J., An, J. Y., et al. (2014). Psychological and social factors affecting internet searches on suicide in Korea: A big data analysis of Google search trends. Yonsei Medical Journal, 55(1), 254–263.

    Article  Google Scholar 

  13. Belaud, J. P., & Dupros, F. (2014). Collaborative simulation and scientific big data analysis: Illustration for sustainability in natural hazards management and chemical process engineering. Computers in Industry, 65(3), 521–535.

    Article  Google Scholar 

  14. Medeiros, B. C., Satram-Hoang, S., Hurst, D., et al. (2015). Big data analysis of treatment patterns and outcomes among elderly acute myeloid leukemia patients in the United States. Annals of Hematology, 94(7), 1127–1138.

    Article  Google Scholar 

  15. Song, T. M., & Ryu, S. (2015). Big data analysis framework for healthcare and social sectors in Korea. Healthcare Informatics Research, 21(1), 3–9.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. State Key Laboratory of Mathematical Engineering and Advanced Computing, Zhengzhou, 450001, China

    Wenjie Zhang & Liehui Jiang

Authors
  1. Wenjie Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Liehui Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Wenjie Zhang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, W., Jiang, L. Algorithm Analysis for Big Data in Education Based on Depth Learning. Wireless Pers Commun 102, 3111–3119 (2018). https://doi.org/10.1007/s11277-018-5331-3

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-018-5331-3

Keywords

Search

Navigation