Abstract

The tourism industry has witnessed a paradigm shift with the advent of Big Data and scientific computing visualization algorithms. This paper explores the application of these technologies in tourism management, aiming to enhance decision-making processes, optimize resource allocation, and improve the overall tourist experience. Big Data analytics provide insights into tourist behaviours, preferences, and trends by analyzing vast amounts of data collected from various sources such as social media, mobile apps, and booking platforms. Scientific computing visualization algorithms then translate these insights into actionable strategies by generating intuitive visual representations of complex data sets. This paper reviews the integration of Big Data analytics and scientific computing visualization algorithms in key areas of tourism management, including destination planning, marketing campaigns, pricing strategies, and resource allocation. It highlights the benefits of using these technologies, such as personalized recommendations, targeted marketing efforts, dynamic pricing models, and efficient resource utilization. Furthermore, the paper discusses the challenges and limitations associated with implementing these technologies in the tourism industry, such as data privacy concerns, technological infrastructure requirements, and the need for skilled professionals. It concludes by emphasizing the importance of adopting a holistic approach that combines advanced technologies with domain expertise to unlock the full potential of Big Data in tourism management.

Details

Title
Application of Big Data Tourism Management Based on Scientific Computing Visualization Algorithms
Author
Shi, Qingbo 1 

 Department of Tourism Management, Zibo Vocational Institute, Zibo, Shandong, 255314, China 
Pages
603-610
Publication year
2024
Publication date
2024
Publisher
Engineering and Scientific Research Groups
e-ISSN
11125209
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
3081429746
Copyright
© 2024. This work is published under https://creativecommons.org/licenses/by/4.0/legalcode (the“License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.