About | Contact Us | Register | Login
ProceedingsSeriesJournalsSearchEAI
Multimedia Technology and Enhanced Learning. 5th EAI International Conference, ICMTEL 2023, Leicester, UK, April 28-29, 2023, Proceedings, Part I

Research Article

An Intelligent Mining Method of Distributed Data Based on Multi-agent Technology

Cite
BibTeX Plain Text
  • @INPROCEEDINGS{10.1007/978-3-031-50571-3_22,
        author={Zhongwei Chen and Xiaofeng Li},
        title={An Intelligent Mining Method of Distributed Data Based on Multi-agent Technology},
        proceedings={Multimedia Technology and Enhanced Learning. 5th EAI International Conference, ICMTEL 2023, Leicester, UK, April 28-29, 2023, Proceedings, Part I},
        proceedings_a={ICMTEL},
        year={2024},
        month={2},
        keywords={Multi-Agent Technology Distributed Data Data Mining Intelligent Mining},
        doi={10.1007/978-3-031-50571-3_22}
    }
    
  • Zhongwei Chen
    Xiaofeng Li
    Year: 2024
    An Intelligent Mining Method of Distributed Data Based on Multi-agent Technology
    ICMTEL
    Springer
    DOI: 10.1007/978-3-031-50571-3_22
Zhongwei Chen1, Xiaofeng Li1,*
  • 1: College of Information Engineering, Guangxi University of Foreign Languages
*Contact email: zhongjie669966@163.com

Abstract

In order to solve the above problems, a new distributed data intelligent mining method based on Multi-Agent technology is studied. Collect distributed data, complete data storage through compression, archiving and storage, use real-time data monitoring architecture to determine data status, implement information collection through data analysis and extraction, input data to filter corresponding parameters, build energy consumption statistical reports to improve report functions, establish mining models, determine historical data, and analyze historical data. Analyze the trend of data energy consumption, and realize digital information analysis according to the change probability of data energy consumption. According to the essential characteristics, application-related characteristics, performance characteristics and acquisition characteristics of the equipment, realize data preprocessing, use mapping processing to complete the missing trapping, extract the main information, and project the data to the 1-dimensional to 3-dimensional space to obtain the initial model. The model mines key factors, selects random variables, analyzes the distribution mode of the network structure, realizes parameter independence, establishes a relational model, and obtains the mining results. The experimental results show that the distributed data intelligent mining method based on Multi-Agent technology can effectively shorten the mining time, and the accuracy rate of mining results is as high as 90%, which can provide good theoretical support.

Keywords
Multi-Agent Technology Distributed Data Data Mining Intelligent Mining
Published
2024-02-21
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-031-50571-3_22
Copyright © 2023–2025 ICST
EBSCOProQuestDBLPDOAJPortico
EAI Logo

About EAI

  • Who We Are
  • Leadership
  • Research Areas
  • Partners
  • Media Center

Community

  • Membership
  • Conference
  • Recognition
  • Sponsor Us

Publish with EAI

  • Publishing
  • Journals
  • Proceedings
  • Books
  • EUDL