Advanced Hybrid Information Processing. Second EAI International Conference, ADHIP 2018, Yiyang, China, October 5-6, 2018, Proceedings

Research Article

Research on Data Management System for Drug Testing Based on Big Data

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  • @INPROCEEDINGS{10.1007/978-3-030-19086-6_64,
        author={Fu-yong Bian and Ming Zhang and Zhen Chen and Rong Xu},
        title={Research on Data Management System for Drug Testing Based on Big Data},
        proceedings={Advanced Hybrid Information Processing. Second EAI International Conference, ADHIP 2018, Yiyang, China, October 5-6, 2018, Proceedings},
        proceedings_a={ADHIP},
        year={2019},
        month={5},
        keywords={Big data Drug testing Data management .NET framework B/S module MyEclipse node Detection of circulation E-R data},
        doi={10.1007/978-3-030-19086-6_64}
    }
    
  • Fu-yong Bian
    Ming Zhang
    Zhen Chen
    Rong Xu
    Year: 2019
    Research on Data Management System for Drug Testing Based on Big Data
    ADHIP
    Springer
    DOI: 10.1007/978-3-030-19086-6_64
Fu-yong Bian1,*, Ming Zhang1, Zhen Chen1, Rong Xu2,*
  • 1: Chuxiong Medical College
  • 2: Anhui Radio and TV University
*Contact email: dfjpds55454@sina.com, xurong528528@163.com

Abstract

The traditional drug detection data management system has the disadvantages of limited submenu generation and uneven distribution of management rights. In order to solve these problems, a new data management system based on big data is designed. Through the two steps of .NET framework and B/S detection module design, the hardware operation environment of the new system is completed. On this basis, determine the MyEclipse node and the detection process. Under this precondition, all the process parameters related to drug data are stored in the system database for a long time, and the total amount of E-R data can be determined, and then the design of drug testing data management system can be completed. The experimental results show that compared with the traditional system, the management authority distribution uniformity of the system can reach 81.57%, which is much higher than that of the traditional method. The application of the new system can effectively improve the sub-menu generation rate.