大数据 ›› 2017, Vol. 3 ›› Issue (2): 78-91.doi: 10.11959/j.issn.2096-0271.2017021

• 应用 • 上一篇    下一篇

基于校园一卡通数据好友发现及应用

鲁鸣鸣,张丹,王建新   

  1. 中南大学信息科学与工程学院,湖南 长沙 410083
  • 出版日期:2017-03-20 发布日期:2017-03-24
  • 作者简介:鲁鸣鸣(1978-),男,博士,中南大学信息科学与工程学院副教授,中国计算机学会会员,主要研究方向为深度学习及其在大数据、城市计算、移动计算和物联网方面的应用。|张丹(1991-),女,中南大学信息与工程学院硕士生,主要研究方向为大数据、数据分析。|王建新(1969-),博士,中南大学信息科学与工程学院教授,中国计算机学会高级会员,主要研究方向为计算机算法、网络优化理论、生物信息学。
  • 基金资助:
    国家自然科学基金资助项目(No.61232001);国家自然科学基金资助项目(No.60903222);国家自然科学基金资助项目(No.61173169);湖南省战略新兴产业重大科技研究计划基金资助项目(No.208GK4054)

Smart-card based campus friend mining and its applications

Mingming LU,Dan ZHANG,Jianxin WANG   

  1. School of Information Science and Engineering,Central South University,Changsha 410083,China
  • Online:2017-03-20 Published:2017-03-24
  • Supported by:
    The National Natural Science Foundation of China(No.61232001);The National Natural Science Foundation of China(No.60903222);The National Natural Science Foundation of China(No.61173169);Hunan Strategic Emerging Industries Major Scientific and Technological Research Project(No.208GK4054)

摘要:

教育大数据是近年来的研究热点。利用分布式文件系统对校园一卡通数据进行存储、预处理和分布式计算。在此基础上,提出学生生活轨迹中的相遇模型,从而挖掘学生线下社交关系。为了区分熟悉的陌生人和真正的好友,从单个学生和整个班级两个角度分析学生线下相遇行为,既可以挖掘好友关系(包括比较孤立的学生),又可以为校园班级社群管理提供数据支撑。实验结果表明,挖掘出的社交关系比较符合实际情况。

关键词: 社群发现, 相遇模型, 线下社交关系

Abstract:

Recently,educational big data has become a hot topic.A distributed file system to store,preprocess,and analyze campus card data was adopted.Based on it,a student encounter model has been proposed,so as to mine students’ offline social relations.To distinguish real friends from familiar strangers,the offline social relations for either individual students or classes were analyzed.Through these two perspectives,the students’ offline encounters was analyzed,which can not only extract social relationship between friends (including the isolated students),but also provide data support for the campus class management.The experimental results show that the mined social relations reflect the real relationship.

Key words: community discovery, encounter model, offline social relation

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