Research on Multi source Data Fusion Methods and Their Applications in Data Science

Wenyan Zhang 

University of Nottingham，NG7 2RD 

Abstract:This article explores the characteristics, existing problems, and corresponding optimization strategies of multi-source data fusion in data science. Firstly, it analyzes the diversity, complementarity, dynamism, privacy, and security features of multi-source data fusion. The second is to point out the problems of data quality and consistency, complexity of fusion algorithms, model generalization ability, as well as ethical and legal challenges. The third is to propose optimization measures such as preprocessing and cleaning techniques, efficient fusion algorithm development, adaptive fusion framework, and establishment of ethical and legal frameworks, aiming to improve the efficiency, accuracy, and compliance of multi-source data fusion and promote further development of data science.

Keywords: data science; Multi source data fusion; characteristic; problem 

References 

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Author introduction: Zhang Wenyan (May 28, 2000), female, Han ethnicity, native place: Chongqing, education: master's degree in progress, research direction: Data Science.