[1]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),2014,44(5):907-911.[doi:10.3969/j.issn.1001-0505.2014.05.006]
 Yang Jie,Li Xiaoping,Pan Liya.Semantic fusion method for heterogeneous big data of traffic monitoring systems[J].Journal of Southeast University (Natural Science Edition),2014,44(5):907-911.[doi:10.3969/j.issn.1001-0505.2014.05.006]
点击复制

面向道路交通监控网的异构大数据语义融合方法()
分享到:

《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
44
期数:
2014年第5期
页码:
907-911
栏目:
计算机科学与工程
出版日期:
2014-09-20

文章信息/Info

Title:
Semantic fusion method for heterogeneous big data of traffic monitoring systems
作者:
杨杰12李小平1潘丽娅3
1东南大学计算机科学与工程学院, 南京 211189; 2江苏省公安厅, 南京 210024; 3公安部信息中心, 北京 100741
Author(s):
Yang Jie12 Li Xiaoping1 Pan Liya3
1School of Computer Science and Engineering, Southeast University, Nanjing 211189, China
2Public Security Bureau of Jiangsu Province, Nanjing 210024, China
3Information Center of the Ministry of Public Security of China, Beijing 100741, China
关键词:
大数据 语义融合 MapReduce ACO算法
Keywords:
big data semantic fusion MapReduce ACO(ant colony optimization)
分类号:
TP391
DOI:
10.3969/j.issn.1001-0505.2014.05.006
摘要:
为解决广域网分布式环境下异构车辆轨迹大数据的语义融合问题,基于MapReduce和ACO算法提出可在广域网环境分布式并行执行的异构大数据语义聚类融合DPACO方法.该方法在数据源端节点并行完成聚类运算中复杂度最高的部分,将所得结果合并为数据量较小的中间结果,然后将中间结果传送到中心节点并自适应地生成聚类中心.此外,该方法无需预设公共语义模型,通过移动计算避免移动大数据,大大提高了运算效率.实验比较了DPACO方法和已有基于MapReduce的并行化ACO方法,结果表明DPACO方法在广域网环境异构大数据语义融合中具有更好的可用性.
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(distributed 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 MapReduce. The results demonstrate the better performance of the proposed method in WAN.

参考文献/References:

[1] Li X L, Han J W, Lee J G, et al. Traffic density-based discovery of hot routes in road networks[C]//Proceedings of the 10th International Symposium on Advances in Spatial and Temporal Databases. Boston, MA, USA, 2007: 441-459.
[2] Gonzalez H, Han J W, Li X L, et al. Adaptive fastest path computation on a road network: a traffic mining approach[C]//Proceedings of the 33rd International Conference on Very Large Data Bases. Vienna, Austria, 2007: 794-805.
[3] Jindal T, Giridhar P, Tang L A, et al. Spatiotemporal periodical pattern mining in traffic data[C]//Proceedings of the 2nd ACM SIGKDD International Workshop on Urban Computing. Chicago, IL, USA, 2013: 11-01-11-07.
[4] Tang L A, Zheng Y, Yuan J, et al. On discovery of traveling companions from streaming trajectories[C]//Proceedings of the 28th IEEE International Conference on Data Engineering. Washington, DC, USA, 2012: 186-197.
[5] Deng Z H, Tang S W, Zhang M, et al. Overview of ontology[J]. Acta Scientiarum Naturalium Universitatis Pekinensis, 2002, 38(5): 730-738.
[6] Cannataro M, Talia D. Semantics and knowledge grids: building the next-generation grid[J]. IEEE Intelligent Systems, 2004, 19(1): 56-63.
[7] Tan R, Xing G L, Yuan Z H, et al. System-level calibration for data fusion in wireless sensor networks[J]. ACM Transactions on Sensor Networks, 2013, 9(3): 28-1-28-27.
[8] Kim Y, Shim K, Kim M S, et al. DBCURE-MR: an efficient density-based clustering algorithm for large data using MapReduce[J]. Information Systems, 2014, 42: 15-35.
[9] Song W, Park S C. Latent semantic analysis for vector space expansion and fuzzy logic-based genetic clustering[J]. Knowledge and Information Systems, 2010, 22(3): 347-369.
[10] Cheng X G, Xiao N F. Parallel implementation of dynamic positive and negative feedback ACO with iterative MapReduce model[J]. Journal of Information and Computational Science, 2013, 10(8): 2359-2370.

备注/Memo

备注/Memo:
收稿日期: 2014-05-27.
作者简介: 杨杰(1980—),男,博士生;李小平(联系人),男,博士,教授,博士生导师,xpli@seu.edu.cn.
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).
引用本文: 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,2014,44(5):907-911. [doi:10.3969/j.issn.1001-0505.2014.05.006]
更新日期/Last Update: 2014-09-20