Open Access
SHS Web of Conf.
Volume 92, 2021
The 20th International Scientific Conference Globalization and its Socio-Economic Consequences 2020
Article Number 02002
Number of page(s) 11
Section Behavioral Economics and Decision-Making
Published online 13 January 2021
  1. Ip, R., Ang, L., Seng, K., Broster, C., Pratley, E. (2020). Big educational data & analytics: Survey, architecture and challenges. IEEE access, 8, 116392-116414. [CrossRef] [Google Scholar]
  2. Kwon, Y. O. (2013). Data analytics in education: Current and future directions. Journal of Intelligence and Information Systems, 19(2), 87–99. [CrossRef] [Google Scholar]
  3. Daniel, B. (2014). Big data and analytics in higher education: Opportunities and challenges. British Journal of Educational Technology, 46(5), 1-17. [Google Scholar]
  4. Matsebula, F., Mnkandla, E. (2017). A big data architecture for learning analytics in higher education. In D. R. Cornish (Ed.), 2017 IEEE AFRICON Conference (pp. 951-956). Cape Town: IEEE. [CrossRef] [Google Scholar]
  5. Shacklock, X. (2016). From bricks to clicks: The potential of data and analytics in higher education. London: Higher Education Commission. [Google Scholar]
  6. Jha, S., Jha, M., O’Brien, L. (2018). A step towards big data architecture for higher education analytics. In 5th Asia-Pacific World Congress on Computer Science and Engineering (APWC on CSE) (pp.178-183). Nadi: IEEE. [Google Scholar]
  7. Viberg, O., Hatakka, M., Balter, O., Mavroudi, A. (2018). The current landscape of learning analytics in higher education. Computers in Human Behavior, 89, 98-110. [CrossRef] [Google Scholar]
  8. Siemens, G., Long, P. (2011). Penetrating the fog: analytics in learning and education. Educause Review, 46(5), 30–40. [Google Scholar]
  9. Alley, G. (2019, March 4). What is big data architecture? Big data zone. Retrieved from : [Google Scholar]
  10. Leitner, P., Khalil, M., Ebner, M. (2017). Learning analytics in higher education - A literature review. Switzerland: Springer International Publishing AG. [Google Scholar]
  11. Ang, K. L, Ge, F. L., Seng, K. P. (2020). Big educational data & analytics: Survey, architecture and challenges. IEEE Access, 8(1), 116392-116414. [Google Scholar]
  12. Gong, Y., Janssen, M. (2020). Roles and capabilities of enterprise architecture in big data analytics technology adoption and implementation. Journal of theoretical and applied electronic commerce research, 16(1), 37-51. [CrossRef] [Google Scholar]
  13. Moorman, C. (2020, September 15). Data ingestion: the first step to a sound data strategy. Stitchdata. Retrieved from : [Google Scholar]
  14. Lee, J., Wei, T., Mukhiya, S. K. (2018). Hands-on big data modeling. Effective database design techniques for data architects and business intelligence professionals. Birmingham: Packt Publishing Ltd. [Google Scholar]
  15. Singh, C., Kumar, M. (2019). Mastering Hadoop 3: Big data processing at scale to unlock unique business insights. Birmingham: Packt Publishing Ltd. [Google Scholar]
  16. Shekhar, C. (2019, August 20). Big data ingestion: Parameters, challenges, and best practices. Datapine. Retrieved from : [Google Scholar]
  17. Hadwer, A., Gillis, D., Rezania, D. (2019). Big data analytics for higher education in the cloud era. In 2019 IEEE 4th International Conference on Big Data Analytics (pp. 203-207). Suzhou: IEEE. [CrossRef] [Google Scholar]
  18. Banica, L., Radulescu, M. (2015). Using big data in the academic environment. Procedia of Economics and Finance, 33, 277-286. [CrossRef] [Google Scholar]
  19. Williamson, B. (2018). The hidden architecture of higher education: building a big data infrastructure for the smarter university. International Journal of Education technologies for higher education, 15(12), 1-26. [CrossRef] [Google Scholar]
  20. Demertzis, K., Iliadis, L., Anezakis, V. D. (2019). A machine hearing framework for real-time streaming analytics using lambda architecture. In J. Macintyre et al. (Eds.), International Conference on Engineering Applications of Neural Networks (pp. 246-261). Springer, Cham. [CrossRef] [Google Scholar]
  21. Amare, M. Y., Simonova, S. (2019). Overview of big data challenges, opportunities and its applications in the context of public administration organizations. In K. S. Soliman (Ed.), Proceedings of the 34rd International Business Information Management Association Conference 2019 (IBIMA) (pp. 12200 – 12209), Madrid: IBIMA Publishing. [Google Scholar]
  22. Matacuta, A., Popa, C. (2018). Big data analytics: Analysis of features and performance of big data ingestion tools. Informatica Economica, 22(2), 25-34. [CrossRef] [Google Scholar]
  23. Isaacson, C. (2014). Understanding big data scalability: Big data scalability series, Part I. New Jersey: Pearson Education. [Google Scholar]
  24. Osman, A. M. S. (2019). A novel big data analytics framework for smart cities. Future Generation Computer Systems, 91, 620-633. [CrossRef] [Google Scholar]
  25. Wang, Y., Kung, L., Byrd, T. A. (2018). Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations. Technological Forecasting and Social Change, 126, 3-13. [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.