摘要
随着大数据技术的发展和交通数据量迅速膨胀的挑战,对海量交通数据进行伴随车挖掘已然成为研究热点。提出一种基于Spark计算框架的频繁项集挖掘算法应用于伴随车挖掘模块当中,对海量的卡口交通数据进行Hadoop分布式文件存储(HDFS),并将伴随车挖掘结果可视化地展示在集成系统当中。以实际项目为依托,从而验证该伴随车模块的实现具有实际意义,并可为交通管理者提供科学的辅助决策。
With the development of big data technology and the challenge of the rapid expansion of traffic data, escort vehicle data mining to the massive traffic data has become a hot research area. In this paper, a frequent itemset mining （FIM） algorithm based on Spark computing framework is proposed, which is applied to the escort vehicle mining module, using HDFS to store the massive traffic bayonet data and visualization display the result of escort vehicle mining in the integrated system. Based on the actual project, this paper proves that the verification of the escort vehicle mining module has practical significance, and can provide scientific auxiliary decision for the traffic management.
出处
《计算机应用与软件》
2017年第4期60-64,共5页
Computer Applications and Software
基金
上海市科学技术委员会应用技术开发专项(2014-104)
