| Title | Big Data Analytics Framework for Smart Universities Implementations |
|---|---|
| ID_Doc | 40502 |
| Authors | Shamsuddin, NT; Aziz, NIA; Cob, ZC; Ab Ghani, NL; Drus, SM |
| Title | Big Data Analytics Framework for Smart Universities Implementations |
| Year | 2019 |
| Published | |
| 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. |
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