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sis 23(6):

Research Article

Database System Based on 3Dmax Big Data Mining Technology

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  • @ARTICLE{10.4108/eetsis.3727,
        author={Xiaoyu Chen and Junkai Zhang and Pengshan Ren and Xian Hua and Yanfeng Ni},
        title={Database System Based on 3Dmax Big Data Mining Technology},
        journal={EAI Endorsed Transactions on Scalable Information Systems},
        volume={10},
        number={6},
        publisher={EAI},
        journal_a={SIS},
        year={2023},
        month={9},
        keywords={Frequent item set, 3Dmax, FP-Growth, Big data mining, Map Reduce Programming Model},
        doi={10.4108/eetsis.3727}
    }
    
  • Xiaoyu Chen
    Junkai Zhang
    Pengshan Ren
    Xian Hua
    Yanfeng Ni
    Year: 2023
    Database System Based on 3Dmax Big Data Mining Technology
    SIS
    EAI
    DOI: 10.4108/eetsis.3727
Xiaoyu Chen1, Junkai Zhang1,*, Pengshan Ren1, Xian Hua1, Yanfeng Ni1
  • 1: Henan Institute of Technology
*Contact email: jk09060421@163.com

Abstract

INTRODUCTION: This project intends to study the mining method of FP-growth frequent items in 3Dmax big data under the Hadoop framework and combined with the Map Reduce development model. Firstly, the transaction database is selected according to the frequency of each transaction and the corresponding projection library is generated. Then the obtained image database is distributed on each node computer. Then, under the guidance of the node machine, the projection is divided into different regions to produce several smaller sub-databases. The method is parallelized by using node machine to generate local frequency items. Finally, all the local frequency sets are merged into one complete frequency set. This method does not need to generate as many FP trees as the regular FP-growth method. This method can overcome the computational failure problem caused by the limited memory of a single computer by the conventional FP-Growth method and other methods. At the same time, because the sublibraries of partitions are similar in size, the load distributed to each node machine is more balanced. The effectiveness of the algorithm is improved.

Keywords
Frequent item set, 3Dmax, FP-Growth, Big data mining, Map Reduce Programming Model
Received
2023-08-16
Accepted
2023-09-19
Published
2023-09-19
Publisher
EAI
http://dx.doi.org/10.4108/eetsis.3727

Copyright © 2023 X. Chen et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.

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