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数据管理技术的新格局
引用本文：覃雄派,王会举,李芙蓉,李翠平,陈红,周烜,杜小勇,王珊.数据管理技术的新格局[J].软件学报,2013,24(2):175-197.
作者姓名：覃雄派  王会举  李芙蓉  李翠平  陈红  周烜  杜小勇  王珊
作者单位：教育部数据工程与知识工程重点实验室(中国人民大学),北京 100872;萨师烜大数据管理与分析研究中心(中澳),北京 100872;中国人民大学 信息学院,北京 100872;教育部数据工程与知识工程重点实验室(中国人民大学),北京 100872;萨师烜大数据管理与分析研究中心(中澳),北京 100872;中国人民大学 信息学院,北京 100872;教育部数据工程与知识工程重点实验室(中国人民大学),北京 100872;萨师烜大数据管理与分析研究中心(中澳),北京 100872;中国人民大学 信息学院,北京 100872;教育部数据工程与知识工程重点实验室(中国人民大学),北京 100872;萨师烜大数据管理与分析研究中心(中澳),北京 100872;中国人民大学 信息学院,北京 100872;教育部数据工程与知识工程重点实验室(中国人民大学),北京 100872;萨师烜大数据管理与分析研究中心(中澳),北京 100872;中国人民大学 信息学院,北京 100872;教育部数据工程与知识工程重点实验室(中国人民大学),北京 100872;萨师烜大数据管理与分析研究中心(中澳),北京 100872;中国人民大学 信息学院,北京 100872;教育部数据工程与知识工程重点实验室(中国人民大学),北京 100872;萨师烜大数据管理与分析研究中心(中澳),北京 100872;中国人民大学 信息学院,北京 100872;教育部数据工程与知识工程重点实验室(中国人民大学),北京 100872;萨师烜大数据管理与分析研究中心(中澳),北京 100872;中国人民大学 信息学院,北京 100872
基金项目：国家自然科学基金(61070054,60873017,61170013);“核高基”国家科技重大专项(2010ZX01042-001-002,2010ZX01042-002-002-03);EMC中国研究院“EMC全球CTO办公室”资金
摘    要：数据获取技术的革命性进步、存储器价格的显著下降以及人们希望从数据中获得知识的客观需要等,催生了大数据.数据管理技术迎来了大数据时代.关系数据库技术经历了20世纪70年代以来40年的发展,目前遇到了系统扩展性不足、支持数据类型单一等困难.近年来,noSQL技术异军突起,对多种类型的数据进行有效的管理、处理和分析;通过并行处理技术获得良好的系统性能;并以其高度的扩展性,满足不断增长的数据量的处理要求.试图沿着数据库技术进步的历史脉络,从应用维度(操作型与分析型应用)入手,为读者展开当今数据管理技术的新格局,讨论具有挑战性的重要问题,并介绍作者自己的研究工作.
关 键 词：关系数据库  noSQL  大数据  操作型  分析型  新格局
收稿时间：2012/6/12 0:00:00
修稿时间：2012/10/16 0:00:00

New Landscape of Data Management Technologies
QIN Xiong-Pai,WANG Hui-Ju,LI Fu-Rong,LI Cui-Ping,CHEN Hong,ZHOU Xuan,DU Xiao-Yong and WANG Shan.New Landscape of Data Management Technologies[J].Journal of Software,2013,24(2):175-197.
Authors:QIN Xiong-Pai  WANG Hui-Ju  LI Fu-Rong  LI Cui-Ping  CHEN Hong  ZHOU Xuan  DU Xiao-Yong and WANG Shan
Affiliation:Key Laboratory of Data Engineering and Knowledge Engineering (Renmin University of China), Ministry of Education, Beijing 100872, China;Sa Shi-Xuan Big Data Management and Analytics Research Center (Sino-Australia), Beijing 100872, China;Information School, Renmin University of China, Beijing 100872, China;Key Laboratory of Data Engineering and Knowledge Engineering (Renmin University of China), Ministry of Education, Beijing 100872, China;Sa Shi-Xuan Big Data Management and Analytics Research Center (Sino-Australia), Beijing 100872, China;Information School, Renmin University of China, Beijing 100872, China;Key Laboratory of Data Engineering and Knowledge Engineering (Renmin University of China), Ministry of Education, Beijing 100872, China;Sa Shi-Xuan Big Data Management and Analytics Research Center (Sino-Australia), Beijing 100872, China;Information School, Renmin University of China, Beijing 100872, China;Key Laboratory of Data Engineering and Knowledge Engineering (Renmin University of China), Ministry of Education, Beijing 100872, China;Sa Shi-Xuan Big Data Management and Analytics Research Center (Sino-Australia), Beijing 100872, China;Information School, Renmin University of China, Beijing 100872, China;Key Laboratory of Data Engineering and Knowledge Engineering (Renmin University of China), Ministry of Education, Beijing 100872, China;Sa Shi-Xuan Big Data Management and Analytics Research Center (Sino-Australia), Beijing 100872, China;Information School, Renmin University of China, Beijing 100872, China;Key Laboratory of Data Engineering and Knowledge Engineering (Renmin University of China), Ministry of Education, Beijing 100872, China;Sa Shi-Xuan Big Data Management and Analytics Research Center (Sino-Australia), Beijing 100872, China;Information School, Renmin University of China, Beijing 100872, China;Key Laboratory of Data Engineering and Knowledge Engineering (Renmin University of China), Ministry of Education, Beijing 100872, China;Sa Shi-Xuan Big Data Management and Analytics Research Center (Sino-Australia), Beijing 100872, China;Information School, Renmin University of China, Beijing 100872, China;Key Laboratory of Data Engineering and Knowledge Engineering (Renmin University of China), Ministry of Education, Beijing 100872, China;Sa Shi-Xuan Big Data Management and Analytics Research Center (Sino-Australia), Beijing 100872, China;Information School, Renmin University of China, Beijing 100872, China
Abstract:The revolutionary progress of data collecting techniques, dramatic decrease of the price of storage devices, as well as the desirability of people to extract information from the data have given birth to the so-called big data and data management technologies usher in the age of big data. RDBMS (relational database management system) undergoes a development of 40 years since the 1970s and now encounters some difficulties such as limited system scalability and limited data variety support. In recent years, noSQL technologies has risen suddenly as a new force. The technologies can manage, process, and analyze various types of data, achieve rather high performance with the help of parallel computing, can handle even bigger volume of data with the nice property of highly scalability. The paper follows the path of database technology progress and unfolds the new landscape of data management technologies from the angle of applications (operational as well as analytic applications). The paper also identifies some chanllenging and important issues that deserve further investigation, with the authors' recent research work introduced at the end.
Keywords:RDBMS (relational database management system)  noSQL  big data  operational  analytic  new landscape
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