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CONCEPTUAL BIG DATA CASE STUDY TO IDENTIFYING RISKS OF NEW NUCLEAR TECHNOLOGIES |
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| รหัสดีโอไอ | |
| Title | CONCEPTUAL BIG DATA CASE STUDY TO IDENTIFYING RISKS OF NEW NUCLEAR TECHNOLOGIES |
| Creator | Jaeheum Yeon, Mark Czarny, John Walewski, Julian Kang |
| Contributor | Didem Ozevin, Hossein Ataei, Mehdi Modares, Asli Pelin Gurgun, Siamak Yazdani, Amarjit Singh |
| Publisher | ISEC PRESS |
| Publication Year | 2562 |
| Keyword | Risk identification, Visual text mining, Content analysis |
| Abstract | New technologies associated with nuclear power plants are being introduced regularly. However, many of the risks and uncertainties associated with these new nuclear technologies have yet to be identified. In this study, the risks related to newlydeveloped nuclear technologies were determined through an extensive review of the extant literature. A qualitative visual content analysis was selected as the research method employed to identify words repeatedly occurring in 147 journal articles. Through this conceptual "big data" approach, frequently mentioned words were identified using a co-occurrence map. The analysis results were then grouped into four categories: fuel resources, operational system designs, nuclear reactor cooling systems, and steam generators. Words used repeatedly to reference these four key categories were determined to also represent potential causes of risk factors. Many texts could be analyzed in a short period of time through the use of visual content analysis software. Frequently emphasized correlating words were then identified. This big data approach can also be applied to additional nuclear power practices to identify other uncertainties. Last, the limitations of a visual content analysis employed as a risk identification approach were revealed through this study. |
| ISBN | 978-0-9960437-6-2 |
| Language | English |
| URL Website | https://www.isec-society.org/ |
| Website title | ISEC Society |
