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Research on the Universal Set Theory of Big Data with Its Application

In: Liss 2023

Author

Listed:
  • Xueyan Li

    (Beijing Union University)

  • Zhuyi Li

    (Renmin University of China)

  • Daqing Gong

    (Beijing Jiaotong University)

Abstract

This study examined the universal linkage of big data-a key technical problem in big data application-in consideration of the human brain’s cognition of the external world. Based on set theory, we used data fields to construct a universal set data description model. We defined the various basic operations of the universal set by analyzing the properties of set elements, the description method, the relationship with the AI algorithm system, and the factor fields of the universal set data. Further, based on the data description model of the universal set, for data barriers—a typical bottleneck—we developed a universal data linkage coordination method based on the idea of multi objective optimization. Then, using rail transit safety chain data as a real-world example, we simulated the linkage analysis process for safety risk factor data based on universal set theory. In this way, this study proposes a feasible strategy for applying universal set theory to big data.

Suggested Citation

  • Xueyan Li & Zhuyi Li & Daqing Gong, 2024. "Research on the Universal Set Theory of Big Data with Its Application," Lecture Notes in Operations Research, in: Daqing Gong & Yixuan Ma & Xiaowen Fu & Juliang Zhang & Xiaopu Shang (ed.), Liss 2023, pages 722-736, Springer.
    • Handle: RePEc:spr:lnopch:978-981-97-4045-1_56
    DOI: 10.1007/978-981-97-4045-1_56
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