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Data Models in Big Data Analysis: Applications and Challenges

In: Proceedings of the 2024 3rd International Conference on Public Service, Economic Management and Sustainable Development (PESD 2024)

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  • Chong Ru

    (The London School of Economics and Political Science)

Abstract

Big data has emerged as a crucial aspect in the digital era, and data models play a vital role in extracting valuable insights from vast amounts of data. This paper focuses on the applications and challenges of data models in big data analysis. It begins by exploring the diverse types of data models commonly used in big data scenarios, such as relational models, NoSQL models, and graph models. The applications range from business intelligence for informed decision-making in enterprises to healthcare for disease prediction and personalized medicine. However, along with the benefits come several challenges. Issues like data quality, scalability, complexity of model selection, and the need for real-time processing pose significant difficulties. This study also delves into recent advancements in addressing these challenges, including the development of hybrid models and the use of machine learning techniques for model optimization. The aim is to provide a comprehensive understanding of how data models are transforming big data analysis and the obstacles that need to be overcome for more efficient and accurate data utilization.

Suggested Citation

  • Chong Ru, 2024. "Data Models in Big Data Analysis: Applications and Challenges," Advances in Economics, Business and Management Research, in: Qiujing Wu & Songsong Liu & Guoliang Wang & Jia Li (ed.), Proceedings of the 2024 3rd International Conference on Public Service, Economic Management and Sustainable Development (PESD 2024), pages 629-635, Springer.
    • Handle: RePEc:spr:advbcp:978-94-6463-598-0_68
    DOI: 10.2991/978-94-6463-598-0_68
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