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A multianalyzer machine learning model for marine heterogeneous data schema mapping.

Wang Yan1, Le Jiajin2, Zhang Yun3

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This study introduces a novel machine learning model for marine data integration, significantly improving schema mapping accuracy. The multianalyzer approach enhances efficiency and reduces errors in heterogeneous data analysis.

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Area of Science:

  • Marine science
  • Data science
  • Computer science

Background:

  • Marine heterogeneous data integration faces challenges in accurate schema mapping.
  • Existing methods lack efficiency and precision in handling diverse data sources.

Purpose of the Study:

  • To propose an efficient and accurate heterogeneous data schema mapping method.
  • To improve the learning results and evaluation of marine data integration.

Main Methods:

  • Developed a heterogeneous data schema mapping method based on a multianalyzer machine learning model.
  • Utilized a fuzzy comprehensive evaluation system for quantitative judging and output evaluation.

Main Results:

  • The multianalyzer model comprehensively analyzes learning results.
  • Experimental comparison on East China Sea observing data confirmed the model's effectiveness.
  • Demonstrated a significant improvement in mapping error rate reduction.

Conclusions:

  • The proposed multianalyzer machine learning model effectively addresses challenges in marine heterogeneous data integration.
  • The method offers enhanced efficiency and accuracy in schema mapping.
  • Fuzzy comprehensive evaluation provides robust multi-factor quantitative judgment.