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, access via your institution.
Buy single article
Instant access to the full article PDF.
Price includes VAT (USA)
Tax calculation will be finalised during checkout.
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.
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.
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.
Fan, J., Han, F., & Liu, H. (2014). Challenges of big data analysis. National Science Review, 1(2), 293–314.
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.
Shim, K. (2013). MapReduce algorithms for big data analysis. Proceedings of the VLDB Endowment, 5(12), 2016–2017.
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.
Zhang, Y., Chen, M., Mao, S., et al. (2014). CAP: Community activity prediction based on big data analysis. Network IEEE, 28(4), 52–57.
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.
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.
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.
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.
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.
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.
Song, T. M., & Ryu, S. (2015). Big data analysis framework for healthcare and social sectors in Korea. Healthcare Informatics Research, 21(1), 3–9.
About this article
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
- Map Reduce
- Depth learning
- College data
- Algorithm analysis