Skip to main content

Big Data Analytics Framework for Smart Universities Implementations

  • Conference paper
  • First Online:
Proceedings of the 3rd International Symposium of Information and Internet Technology (SYMINTECH 2018) (SYMINTECH 2018)

Abstract

Advance integration progress between multiple main areas such as economy, mobility, environment, people, living and government is the establishment of Smart City. It is also collaborate both ICT and urban studies based sections. In a mean time, big data has been applied into multiple fields such as the healthcare, government, e-commerce and universities. In addition, the evolution of Internet of Things (IoT) technologies coupled with big data capability have open up to new possibilities for smart city implementation. Further to this, technologies such as advance server and classroom with smart technologies are helping universities with its function. In order to enhance Research and Innovation department in universities, specific objectives, reliable staffs and efficient standard of procedure (SOP) is needed for becoming smart universities. Big data analytics shows promise at universities today as they have access to large amount of data resulted from their teaching and learning activities. Data analytics can be used to provide insights for the betterment of the students and staffs, to improve the teaching and learning process, and for supporting management decision making needs. However, there is limited discussion on how big data can be implemented in education domain to make it smarter, especially related to critical components for a successful smart university implementation. In this paper, we describe the smart city components and smart-based applications used within the context of smart cities. The application of big data analytics to support smart cities are also discussed and finally, a framework of big data analytics for smart university is proposed and main components of the framework are also described based on the review of existing works in literature.

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 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover 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

  • Al Nuaimi, E., Al Neyadi, H., Mohamed, N., Al-Jaroodi, J.: Applications of big data to smart cities. J. Internet Serv. Appl. 6(1), 25 (2015)

    Article  Google Scholar 

  • Alawadhi, S., Scholl, H.J.J.: Aspirations and realizations : the smart city of seattle, pp. 1695–1703 (2013) http://doi.org/10.1109/HICSS.2013.102

  • Belissent, J., Frederic, G.: Service Providers Accelerate Smart City Projects. Foresster, Cambridge (2013)

    Google Scholar 

  • Coccoli, M., Guercio, A., Maresca, P., Stanganelli, L.: Smarter universities: a vision for the fast changing digital era. J. Vis. Lang. Comput. 25, 1003–1011 (2014)

    Article  Google Scholar 

  • Diamantoulakis, P.D., Kapinas, V.M., Karagiannidis, G.K.: Big data analytics for dynamic energy management in smart grids. Big Data Res. 2(3), 94–101 (2015)

    Article  Google Scholar 

  • First LoRA smart campus in Malaysia (2018). http://www.apu.edu.my/media/news/1272. Accessed 18 Dec 2017

  • Gandhi, S.L.: Smart education service model based on IOT technology. In: Paper presented at the International Interdisciplinary Conference on Science Technology Engineering Management Pharmacy and Humanities, Singapore (2017)

    Google Scholar 

  • Geisler, K.: The relationship between smart grids and smart cities. IEEE Smart Grid (2013)

    Google Scholar 

  • Hamzah, H., Adnan, Y.M., Daud, M.N., Alias, A., Dali, M.M.: A smart city assessment framework. Faculty of Built Environment, University of Malaya, Malaysa, Consultado el, 25 (2016)

    Google Scholar 

  • Hashem, I.A.T., Chang, V., Anuar, N.B., Adewole, K., Yaqoob, I., Gani, A., Ahmed, E., Chiroma, H.: The role of big data in smart city. Int. J. Inf. Manag. 36(5), 748–758 (2016)

    Article  Google Scholar 

  • Hayikader, S., Toriq, M., Niyaz, M., Dahlan, A.: Big data and a smarter university: a literature review. Int. J. Sci. Res. Publ. 5(5), 1–4 (2015)

    Google Scholar 

  • Khan, A., Pohl, M., Bosse, S., Hart, S.W., Turowski, K.: A holistic view of the IoT process from sensors to the business value (2017)

    Google Scholar 

  • Liu, P., Peng, Z.: China’s smart city pilots: a progress report. Computer 10, 72–81 (2014)

    Article  Google Scholar 

  • Manville, C., Cochrane, G., Cave, J., Millard, J., Pederson, J.K., Thaarup, R.K., Liebe, A., Wissner, M., Massink, R., Kotterink, B.: Mapping smart cities in the EU (2014)

    Google Scholar 

  • Pinka, K., Kampars, J., Minkevičs, V.: Case study: IoT data integration for higher education institution. Inf. Technol. Manag. Sci. 19(1), 71–77 (2016)

    Google Scholar 

  • Ramaprasad, A., Sánchez-Ortiz, A., Syn, T.: A unified definition of a smart city. In: International Conference on Electronic Government, pp. 13–24. Springer, Cham, September 2017

    Google Scholar 

  • Salleh, M.S., Omar, M.Z.: University-industry collaboration models in Malaysia. Proc. Soc. Behav. Sci. 102, 654–664 (2012). https://doi.org/10.1016/j.sbspro.2013.10.784. Ifee 2012

    Article  Google Scholar 

  • Semanjski, I., Gautama, S.: Smart city mobility application—gradient boosting trees for mobility prediction and analysis based on crowdsourced data. Sensors 15(7), 15974–15987 (2015)

    Article  Google Scholar 

  • Staricco, L.: Smart mobility: opportunities and conditions. J. Land Use Mobility Environ. 6(3), 342–354 (2013)

    Google Scholar 

  • Tahir, Z., Malek, J.A.: Main criteria in the development of smart cities determined using analytical method. Plann. Malaysia J. 14(5), 1–14 (2016)

    Google Scholar 

  • Temkar, R., Gupte, M., Kalgaonkar, S.: Internet of things for smart classrooms. Int. Res. J. Eng. Technol. (2016)

    Google Scholar 

  • Yau, K.L.A., Lau, S.L., Chua, H.N., Ling, M.H., Iranmanesh, V., Kwan, S.C.C.: Greater Kuala Lumpur as a smart city: a case study on technology opportunities. In: 2016 8th International Conference on Knowledge and Smart Technology (KST), pp. 96–101. IEEE, February 2016

    Google Scholar 

  • Yoshikawa, Y.A.S.S.H.M.T.M.Y.: Hitachi’s Vision of the Smart City. Hitachi Rev. 61(3), 111–118 (2012)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Nur Tasnim Shamsuddin .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Shamsuddin, N.T., Aziz, N.I.A., Cob, Z.C., Ghani, N.L.A., Drus, S.M. (2019). Big Data Analytics Framework for Smart Universities Implementations. In: Othman, M., Abd Aziz, M., Md Saat, M., Misran, M. (eds) Proceedings of the 3rd International Symposium of Information and Internet Technology (SYMINTECH 2018). SYMINTECH 2018. Lecture Notes in Electrical Engineering, vol 565. Springer, Cham. https://doi.org/10.1007/978-3-030-20717-5_7

Download citation

Publish with us

Policies and ethics