2019 Vol.2 Dec. No.6

• Title: Research on Feedback Marking Methods for Characteristic Data of Interactive Network in Big Data
• Name: Norbert Seligman
• Company: Holmesglen Institute
• Abstract:

  In order to solve the problem that the conventional interactive network feature data information feedback labeling method has a low success rate of feature data information feedback labeling in a big data environment, a method for interactive network characteristic data information feedback labeling under big data is proposed. Determine whether the primary key for interactive network characteristic data collection is automatically added to complete the collection, identify the collected data, and rely on the big data labeling function to establish labeling rules for characteristic data information feedback. Run the labeling program according to the labeling rules to realize the interactive network characteristic data information feedback labeling under big data. Using the proposed labeling method, the simulation verification is carried out in the big data interactive network characteristic data environment, and the proposed interactive network characteristic data information feedback labeling method is compared with the conventional labeling method. The success rate of feature data information feedback tagging increased by 57.06%, which is suitable for interactive network feature data information feedback tagging under big data.

• Keyword: Interactive Network; Feedback Marking Methods;
• DOI: 10.12250/jpciams2019060636
• Citation form: Norbert Seligman.Research on Feedback Marking Methods for Characteristic Data of Interactive Network in Big Data[J]. Computer Informatization and Mechanical System, 2019, vol. 2, pp. 170-176.

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