| 摘要(英) |
This research is based on the background of the number of people issued by the tourist attractions in Taoyuan City and researches the technology of de-identification of mobile communication through big data data mining method, to collect statistics and analyze the applicable information and transform it into the information provided to the competent authority and the Case study analysis of popular use. To achieve the following two important purposes: In terms of big data mining practice, this study collects and organizes the actual operation of Taoyuan City′s scenic spot information, explores the evolution and improvement of related information, and compares the data released by other central, county and city agencies in Taiwan. The analysis of shortcomings can more effectively achieve the optimization of data mining when providing other similar program planning in the future. Based on the empirical evidence of the case study process, the internal data is collected comprehensively, accurately, and in real-time with the characteristics of big data, and the external traffic warning lights are adjusted to red, yellow, and green according to the actual situation. In addition, real-time image assistance of scenic spots is added. Comparing similar case studies, find out the key factors that can be imitated, and use and integrate them into future development as important practical data in Taiwan′s future smart tourism service transformation plan. The main attraction management lies in the long-term collection of accumulated information by the competent authorities of venues and venues. Traffic control personnel or traffic police units with front-line traffic guidance, and even traffic units related to road planning should be included together as a cooperative unit in the actual measurement, and corrections based on the actual measurement should be included. After that, improving the reliability and validity of the entire data and giving feedback as follow-up optimization is a suitable estimate, and because the estimate application is more convenient, the open data estimate can be applied to more stakeholders and institutions. and participants. |
| 參考文獻 |
參考文獻：
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