Skip to main content

Design of Intelligent Education Management Information System in Colleges and Universities from the Perspective of Big Data

  • Conference paper
  • First Online:
e-Learning, e-Education, and Online Training (eLEOT 2022)

Abstract

There are many internal data types in the education management information system, which leads to the poor management effect of the intelligent education management information system in colleges and universities. Design an intelligent education management information system in Colleges and universities from the perspective of big data. The hardware part of the system focuses on the design of DSP module, FPGA module, analog-to-digital conversion module, A/D conversion module interface circuit and communication interface. In the system software part, the big data technology is used to divide and allocate data sets, so as to realize the design of intelligent education management information system in colleges and universities. Experimental results show that the proposed intelligent education management information system can effectively improve the accuracy and efficiency of resource mining, and has a good effect.

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 109.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 139.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Chen, J., Wang, Z., Mao, T.: Resource management for hybrid RF/VLC V2I wireless communication system. IEEE Commun. Lett. 24(4), 868–871 (2020)

    Article  Google Scholar 

  2. Momblanch, A., et al.: Untangling the water-food-energy-environment nexus for global change adaptation in a complex Himalayan water resource system. Sci. Total Environ. 655(10), 35–47 (2019)

    Article  Google Scholar 

  3. Fu, S., Gao, J., Zhao, L.: Integrated resource management for terrestrial-satellite systems. IEEE Trans. Veh. Technol. 69(3), 3256–3266 (2020)

    Article  Google Scholar 

  4. Schulze, C., Thiede, S., Thiede, B., Kurle, D., Blume, S., Herrmann, C.: Cooling tower management in manufacturing companies: a cyber-physical system approach. J. Clean. Prod. 211(20), 428–441 (2019)

    Article  Google Scholar 

  5. Sinha, D., Roy, R.: Reviewing cyber-physical system as a part of smart factory in industry 4.0. IEEE Eng. Manag. Rev. 48(2), 103–117 (2020)

    Google Scholar 

  6. Hamidpour, H., Aghaei, J., Pirouzi, S., Dehghan, S., Niknam, T.: Flexible, reliable and renewable power system resource planning considering energy storage systems and demand response programs. IET Renew. Power Gener. 13(11), 1862–1872 (2019)

    Article  Google Scholar 

  7. Tinoco, J., de Granrut, M., Dias, D., Miranda, T., Simon, A.-G.: Piezometric level prediction based on data mining techniques. Neural Comput. Appl. 32(8), 4009–4024 (2019). https://doi.org/10.1007/s00521-019-04392-6

    Article  Google Scholar 

  8. Sekiguchi, H., et al.: Computerized data mining analysis of keywords as indicators of the concepts in AHA-BLS guideline updates. Am. J. Emerg. Med. 38(7), 1436–1440 (2020)

    Article  Google Scholar 

  9. Mohamed, A., Molendijk, J., Hill, M.M.: Lipidr: a software tool for data mining and analysis of lipidomics datasets. J. Proteome Res. 19(7), 2890–2897 (2020)

    Article  Google Scholar 

  10. Campo-Vila, J.D., Takilalte, A., Bifet, A., Mora-López, L.: Binding data mining and expert knowledge for one-day-ahead prediction of hourly global solar radiation. Expert Syst. Appl. 167(8), 114147 (2020)

    Google Scholar 

  11. Pang, H., Zhao, W.: Big data efficient hybrid cloud storage model and algorithm simulation under 5 G network. Comput. Simul. 37(7), 350–353, 379 (2020)

    Google Scholar 

Download references

Funding

1. Funded by Beijing Higher Education Association (YB2021113).

2. Funded by research project of Beijing Union University (ZK90202105).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Haiyan Zhao .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zhao, H., Liu, S., Xiao, L., Liu, K., Liu, F. (2022). Design of Intelligent Education Management Information System in Colleges and Universities from the Perspective of Big Data. In: Fu, W., Sun, G. (eds) e-Learning, e-Education, and Online Training. eLEOT 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 453. Springer, Cham. https://doi.org/10.1007/978-3-031-21161-4_26

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-21161-4_26

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-21160-7

  • Online ISBN: 978-3-031-21161-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

pFad - Phonifier reborn

Pfad - The Proxy pFad of © 2024 Garber Painting. All rights reserved.

Note: This service is not intended for secure transactions such as banking, social media, email, or purchasing. Use at your own risk. We assume no liability whatsoever for broken pages.


Alternative Proxies:

Alternative Proxy

pFad Proxy

pFad v3 Proxy

pFad v4 Proxy