Investigation of the Architecture for A Application Creation Platform Leveraging Modern Technology on Smart Campus
Intelligent technologies are driving the development of smart campuses, fostering a dynamic and diverse intelligent environment. However, the current trend of customizing smart campus solutions often positions campus citizens as mere bystanders or end users. To enhance participatory engagement in campus development and increase the autonomy of platform construction, this paper leverages the strengths of big data warehouses, data analytics, the Internet of Things (IoT), and cloud computing—specifically their security, concurrency, reliability, redundancy, and scalability—to propose a campus application creation platform. This platform aims to streamline the software development process, facilitate the publication of software products, and promote functionality sharing and collaborative evolution. We introduce an architectural model comprising four components: the data lake and warehouse layer, the data ferry layer, the resource service layer, and the platform portal. The internal operational mechanisms of this architecture are discussed from the perspectives of "applications as data," self-feedback analysis, application evaluation systems, and system security. The advantages of the platform are analyzed, along with application cases from both teacher and student perspectives. We also explore the platform's significance within the campus context, including its potential to adjust campus functionality, enhance overall innovation capabilities in education, and increase the autonomy of software functionalities. Our findings suggest that a modern application creation platform can significantly facilitate the self-improvement of smart campus functionalities, thereby further enhancing the intelligence of the campus.
Authors
- Xiaosong He (ORCID: https://orcid.org/0009-0008-0586-4808)
- Kai Ma (ORCID: https://orcid.org/0009-0002-1697-665X)
- Hongjing He
- J. Huang
Institutions
- Chongqing University (CN)
Publication Details
- Published
- 2024-10-18
- DOI
- https://doi.org/10.1145/3708036.3708064
- Primary Topic
- AI and Big Data Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00