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挖掘数据关系的食品抽检数据可视化分析图研究
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  • 英文篇名:Visual Analysis Graph Research of Food Sampling Data Based on Mining Data Relationship
  • 作者:杨璐 ; 张馨月 ; 郑丽敏
  • 英文作者:YANG Lu;ZHANG Xinyue;ZHENG Limin;College of Information and Electrical Engineering,China Agricultural University;Beijing Laboratory of Food Quality and Safety;
  • 关键词:食品抽检 ; 数据关系挖掘 ; 可视化
  • 英文关键词:food sampling;;data relationship mining;;visualization
  • 中文刊名:NYJX
  • 英文刊名:Transactions of the Chinese Society for Agricultural Machinery
  • 机构:中国农业大学信息与电气工程学院;食品质量与安全北京实验室;
  • 出版日期:2019-06-25
  • 出版单位:农业机械学报
  • 年:2019
  • 期:v.50
  • 基金:国家重点研发计划项目(2017YFC1601803);; 国家蛋鸡产业技术体系项目(CARS-40-K27)
  • 语种:中文;
  • 页:NYJX201906031
  • 页数:8
  • CN:06
  • ISSN:11-1964/S
  • 分类号:280-287
摘要
食品安全数据具有多源、关联和不确定性等特征,数据的项目、项目的属性以及相链接数目较多,数据内部潜在关系不明晰,需要研究能够进行关系挖掘的可视视图。针对食品安全领域数据分析的实际需求,采用圆环布局、节点链接布局等元素,对数据间的简单关系和层次结构进行展示;结合同心圆布局、散点图、热力图元素和动态过滤以及数据聚类技术,在展示数据节点性质的同时,揭示数据间的潜在关联关系,并综合以上视图提出了一种挖掘数据关系的可视分析图Explore View。应用于国家食品药品监督管理总局抽检数据集,使用立方体隐喻组织数据,二分图定义任务需求,完成可视编码,进行数据关系探索,为可能发生的食品安全事件提供预警,定位重点监管对象,为制定新的食品安全规章制度提供参考。
        The relationship between data can be visualized by using multiple types of images,so it is convenient for users to obtain information and relationships between data. However,when many data items,attributes and links,and the relationships between data are not clear,a visual view which is capable of relationship mining is required. For the real task requirements of domain data analysis,elements such as circle layout and node link layout were used to display simple relationships and hierarchical structures between data. What's more,combining concentric circle layout,scatter plots,thermogram elements and dynamic filtering and data clustering techniques,a relational mining view was proposed that not only demonstrated the nature of data nodes,but also revealed the potential relationships between data. Finally,combining the above views,a visual analysis graph of the mining data relationship was presented,which was ExploreView. It was applied to the sampling data set of the Food and Drug Administration, while using cube metaphor to organize data. The bipartite graph defined task requirements before completing visual coding. It can display the basic situation of data information and dynamically interact according to the actual needs of users,and reflect the attributes,various hierarchical structures and relationships between data. As a result,the visual analysis graph was easy and efficient to operate. It can be used to provide early warning for possible food safety incidents,locate key regulatory targets,which provided reference for the development of rules,and effectively met the needs of different types of users.
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