东南大学学报（自然科学版）Issue(5)：907-911,5.DOI:10.3969/j.issn.1001-0505.2014.05.006
面向道路交通监控网的异构大数据语义融合方法
Semantic fusion method for heterogeneous big data of traffic monitoring systems
摘要
Abstract
To solve the semantic fusion problem among heterogeneous big data captured by traffic monitoring systems and stored in WAN (wide area network),a clustering method DPACO (distrib-uted and parallel ant colony optimization)based on MapReduce and ACO (ant colony optimization) is proposed.The most complex part of clustering is completed in parallel at data source in advance, and the obtained results are merged into small data and sent to central node to adaptively generate the global clustering results.The method avoids presetting semantic models and moving big data is also avoided by mobile computing,which greatly improves the efficiency of clustering.The DPACO method is experimentally compared with an existing parallel clustering algorithm based on MapRe-duce.The results demonstrate the better performance of the proposed method in WAN.关键词
大数据/语义融合/MapReduce/ACO算法Key words
big data/semantic fusion/MapReduce/ACO (ant colony optimization)分类
信息技术与安全科学引用本文复制引用
杨杰,李小平,潘丽娅..面向道路交通监控网的异构大数据语义融合方法[J].东南大学学报（自然科学版）,2014,(5):907-911,5.基金项目
公安部应用创新计划资助项目（2013YYCXJSST044）、江苏省“333高层次人才培养工程”科研资助项目（BRA2013163）． （）
