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刘彩霞. 跨境电商系统用户数据库智能访问方法优化[J]. 科学技术与工程, 2020, 20(1): 265-270.
LIU Caixia.Intelligent access method optimization of user database in cross border electricity supplier system[J].Science Technology and Engineering,2020,20(1):265-270.
跨境电商系统用户数据库智能访问方法优化
Intelligent access method optimization of user database in cross border electricity supplier system
投稿时间:2018-12-11  修订日期:2019-02-17
DOI:
中文关键词:  跨境电商系统 用户数据库 智能 访问
英文关键词:cross border electricity supplier system user database intelligence access
基金项目:河南省高等学校青年骨干教师培养计划项目:大数据视角下电子商务人才生态化培养模式研究(2017GGJS195)
  
作者单位
刘彩霞 郑州工业应用技术学院 信息工程学院
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中文摘要:
      为了解决传统方法不适于大规模用户访问,访问准确性差的问题,通过语义指向性匹配和多维索引树编码结合的方法,对跨境电商系统用户数据库智能访问优化方法进行研究。建立跨境电商数据库模型,为数据库智能访问提供模型依据。依据模糊层次聚类提取语义指向性关联特征,在概念格中完成语义指向性相似度计算,依据提取特征,通过相似度匹配实现数据库智能访问,针对其在用户规模较大时影响访问准确性的问题,采用多维索引树编码的方式对其进行优化,实现对跨境电商系统用户数据库智能访问方法的优化。结果表明:采用所提方法对跨境电商系统时域数据与时频数据语义指向性特征进行提取,能够完成数据语义本体特征指向性聚类,冗余干扰信息被滤除,特征分布聚类性较强;对查全率水平较高情况下的查准率进行测试,发现所提方法在查全率升高时,可令查准率保持在较高的水平,未随查全率的升高有显著下降。可见所提方法访问准确性高。
英文摘要:
      In order to solve the problem that traditional methods are not suitable for large-scale user access and poor access accuracy, this paper studies the intelligent access optimization method of user database in cross-border e-commerce system by means of semantic directivity matching and multi-dimensional index tree coding. A cross border electricity supplier database model is established to provide a model basis for database intelligent access. According to fuzzy hierarchical clustering, semantic directivity association features are extracted, semantic directivity similarity calculation is completed in concept lattice, and database intelligent access is realized by similarity matching based on extracted features. Aiming at the problem that it affects the accuracy of access when the user size is large, multi-dimensional index tree coding is used to encoding it. It is optimized to realize the optimization of the intelligent access method of the user database in the cross-border electricity supplier system. The results show that the proposed method can extract the time-domain data and time-frequency data semantic directivity features of cross-border e-commerce system, and can complete the data semantic ontology feature directivity clustering, the redundant interference information is filtered out, and the clustering of feature distribution is strong; the accuracy rate is tested under the condition of high recall rate, and the proposed method is applied to the cross-border e-commerce system. Methods When the recall rate increased, the accuracy rate could be maintained at a higher level, but did not decrease significantly with the increase of recall rate. The accuracy of the proposed method is high.
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