摘要
现有社交网络数据划分算法大多关注于好友关系和交互关系,忽略了位置信息,造成基于位置查询的响应时间较长。针对该问题,设计了一种移动社交网络双层社交图模型,该模型考虑了移动社交网络中用户交互行为的位置依赖性特点;并在此基础上提出了一种基于位置信息的移动社交网络数据动态划分复制算法MSDPR,该算法采用改进的K-Means算法对位置信息进行聚类,再根据聚类结果对数据进行划分,并利用社交关系进行数据的复制。实验结果表明:MSDPR算法在移动社交网络环境下能够有效地提高本地访问率,降低访问延迟,并且在动态加入数据时具有较好的适应性。
The existing social network data partitioning algorithms focus on the social relationship and interaction, with- out considering location information, which results in the long response time of location-based queries. To solve this problem, we designed a two-layer graph model of mobile social network which takes the location dependency of the user interaction behavior into account. We proposed a mobile SiNS data dynamic partitioning and replication algorithm based on location information-MSDPR. MSDPR divides data based on the clustered results generated by an improved K-Means clustering algorithm, and then replicates data by using the social relationships. Experiments reveal that MSD- PR can effectively improve the efficiency of the local access and reduce the latency of access in the mobile social net- work. Moreover, it also has better adaptability when adding data dynamically.
作者
王青芸
程春玲
WANG Qing-yun CHENG Chun-ling(College of Computer Seienee, Nanjing University of Posts and Teleeommunieations, Nanjing 210003, China)
出处
《计算机科学》
CSCD
北大核心
2017年第3期220-225,共6页
Computer Science
关键词
移动社交网
分布式存储
动态划分复制
位置信息
用户交互
Mobile social network, Distributed storage, Dynamic partitioning and replication, Location information, User mteractlon
