[1]何清.物联网与数据挖掘云服务[J].智能系统学报,2012,7(3):189-194.
HE Qing.The Internet of things and the data mining cloud service[J].CAAI Transactions on Intelligent Systems,2012,7(3):189-194.
点击复制
HE Qing.The Internet of things and the data mining cloud service[J].CAAI Transactions on Intelligent Systems,2012,7(3):189-194.
物联网与数据挖掘云服务
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
7
期数:
2012年第3期
页码:
189-194
栏目:
综述
出版日期:
2012-06-25
- Title:
- The Internet of things and the data mining cloud service
- 文章编号:
- 1673-4785(2012)03-0189-06
- Keywords:
- Internet of Things; cloud computing; data mining; cloud service
- 分类号:
- TP391
- 文献标志码:
- A
- 摘要:
- 物联网与云计算是目前信息技术的研究热点，探讨数据挖掘在其中扮演的角色，以及与这2项技术相结合的方式.分析了数据挖掘在物联网中的地位和作用，指出了云计算是物联网的基石，剖析了分布式数据挖掘与并行数据挖掘的异同，说明了物联网中数据挖掘服务的提供方式.
- Abstract:
- The Internet of things and cloud computing are a hot topic in the field of information technology. Their roles in the application of data mining, their combination, and technologies of data mining were discussed in this paper. Moreover, the status and role of data mining in the Internet of Things were analyzed; it was pointed out that cloud computing is the cornerstone of the Internet of Things. Additionally, the similarities and differences of distributed data mining and parallel data mining were analyzed. Lastly, a way to offer the data mining service in the Internet of Things was described.
- 参考文献/References:
-
［1］邬贺铨．中国物联网应用应该考虑中国国情［EB/OL］. (20100629)［20111125］. http://cloud.csdn.net/a/20100629/267886.html.
［2］李德毅．2012云计算技术发展报告［M］．北京：科学出版社, 2012.
［3］马文方．泛在计算：少谈些概念 多做些实事［N］．中国计算机报, 20100510(38).
［4］张诚，郭毅．数据挖掘与云计算——专访中国科学院计算机研究所何清博士［J］．数字通信, 2011, 38(3): 57.
［5］王媛媛．基于概念格模型的关联规则挖掘算法研究及实现［D］．合肥：合肥工业大学, 2005: 5556.
WANG Yuanyuan. Research and implementation of algorithms of mining association rules based on concept lattice［D］. Hefei: Hefei University of Technology, 2005: 5556.
［6］HE Qing, DU Changying, WANG Qun, et al. A parallel incremental extreme SVM classifier［J］. Neurocomputing, 2011, 74(16): 25322540.
［7］CHU C T, KIM S K, LIN Y A, et al. Mapreduce for machine learning on multicore［C］//Proceedings of the Twentieth Annual Conference on Neural Information Processing Systems. Vancouver, Canada, 2006: 281288.
［8］The Apache Software Foundation. 27 May 2011—Apache Mahout 0.5 released［EB/OL］. ［20111225］. http://mahout.apache.org/.
［9］何清．基于云计算的海量数据挖掘［EB/OL］. (20100525)［20111125］. http://cloud.csdn.net/a/20100525/267105.html.
［10］HE Qing, TAN Qing, MA Xudong, et al. The highactivity parallel implementation of data preprocessing based on MapReduce［C］//The Fifth International Conference on Rough Set and Knowledge Technology (RSKT). Beijing, China, 2010: 646654.
［11］LI Ning, ZENG Li, HE Qing, et al. Parallel implementation of apriori algorithm based on MapReduce［C］//Proceedings of the 13th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing. Kyoto, Japan, 2012(accepted).
［12］HE Qing, ZHUANG Fuzhen, LI Jincheng, et al. Parallel implementation of classification algorithms based on MapReduce［C］//The Fifth International Conference on Rough Set and Knowledge Technology (RSKT). Beijing, China, 2010: 655662.
［13］HE Qing, WANG Qun, DU Changying, et al. A parallel hypersurface classifier for high dimensional data［C］//Proceedings of the 3rd International Symposium on Knowledge Acquisition and Modeling. Wuhan, China, 2010: 338343.
［14］ZHAO Weizhong, MA Huifang, HE Qing. Parallel Kmeans clustering based on mapreduce［C］//The 1st International Conference on Cloud Computing. Beijing, China, 2009: 674679.
［15］HE Qing, MA Yunlong, WANG Qun, et al. Parallel outlier detection using KDtree based on mapreduce［C］//Proceedings of the 2011 IEEE Third International Conference on Cloud Computing Technology and Science. Athen, Greece, 2011: 7580.
- 相似文献/References:
-
[1]黄河燕,曹朝,冯冲.大数据情报分析发展机遇及其挑战[J].智能系统学报,2016,11(6):719.[doi:10.11992/tis.201610025]
HUANG Heyan,CAO Zhao,FENG Chong.Opportunities and challenges of big data intelligence analysis[J].CAAI Transactions on Intelligent Systems,2016,11():719.[doi:10.11992/tis.201610025]
[2]严新平,柳晨光.智能航运系统的发展现状与趋势[J].智能系统学报,2016,11(6):807.[doi:10.11992/tis.201605007]
YAN Xinping,LIU Chenguang.Review and prospect for intelligent waterway transportation system[J].CAAI Transactions on Intelligent Systems,2016,11():807.[doi:10.11992/tis.201605007]
[3]朱正月,陈增强.物联网中的智慧溯源服务系统Petri网建模与分析[J].智能系统学报,2017,12(4):538.[doi:10.11992/tis.201611031]
ZHU Zhengyue,CHEN Zengqiang.Petri net modeling and analysis of an intelligent traceabilityservice system based on the Internet of Things[J].CAAI Transactions on Intelligent Systems,2017,12():538.[doi:10.11992/tis.201611031]
[4]张江强,赵宁,刘文奇.具有两类请求的云计算中心服务器数量的优化[J].智能系统学报,2017,12(5):601.[doi:10.11992/tis.201703042]
ZHANG Jiangqiang,ZHAO Ning,LIU Wenqi.Optimization of the number of servers in a cloud computation center with two demand classes[J].CAAI Transactions on Intelligent Systems,2017,12():601.[doi:10.11992/tis.201703042]
[5]谭旭杰,邓长寿,吴志健,等.云环境下求解大规模优化问题的协同差分进化算法[J].智能系统学报,2018,13(2):243.[doi:10.11992/tis.201706053]
TAN Xujie,DENG Changshou,WU Zhijian,et al.Cooperative differential evolution in cloud computing for solving large-scale optimization problems[J].CAAI Transactions on Intelligent Systems,2018,13():243.[doi:10.11992/tis.201706053]
[6]吴俊伟,姜春茂.负载敏感的云任务三支聚类评分调度研究[J].智能系统学报,2019,14(2):316.[doi:10.11992/tis.201710004]
WU Junwei,JIANG Chunmao.Load-aware score scheduling of three-way clustering for cloud task[J].CAAI Transactions on Intelligent Systems,2019,14():316.[doi:10.11992/tis.201710004]
[7]杨正理,史文,陈海霞,等.大数据背景下高校招生策略预测[J].智能系统学报,2019,14(2):323.[doi:10.11992/tis.201709011]
YANG Zhengli,SHI Wen,CHEN Haixia,et al.The strategy of college enrollment predicted with big data[J].CAAI Transactions on Intelligent Systems,2019,14():323.[doi:10.11992/tis.201709011]
备注/Memo
收稿日期：2011-12-28.网络出版日期：2012-06-08.
基金项目：国家自然科学基金资助项目(60933004,60975039,61175052，61035003,61072085)；国家“863”计划资助项目(2012AA011003).
通信作者：何清. E-mail: heq@ics.ict.ac.cn.
作者简介：
何清，男，1965年生，研究员，博士生导师，中国计算机学会高级会员、人工智能与模式识别专业委员会委员，中国人工智能学会副秘书长、常务理事、知识工程与分布智能专业委员会秘书长、机器学习专业委员会常务委员，中国电子学会云计算专家委员会委员.主要研究方向为机器学习、数据挖掘、文本挖掘、基于云计算的分布式并行数据挖掘等.主持和参与国家“863”和“973”计划、国家自然科学基金等科研项目多项，发表学术论文近百篇.2008年底，何清研究员带领他的中科院计算所数据挖掘团队，受中国移动研究院委托，合作开发完成了基于云计算的并行数据挖掘平台，用于TB级实际数据的挖掘，实现了高性能、低成本的数据挖掘.
更新日期/Last Update:
2012-09-05