Journal of Software:2019.30(1):164-193

(北京大学 信息科学技术学院, 北京 100871;软件开发环境国家重点实验室(北京航空航天大学), 北京 100083;南京航空航天大学 计算机科学与技术学院, 江苏 南京 211106;电子科技大学 计算机科学与工程学院, 四川 成都 611731;北京大学 计算机科学技术研究所, 北京 100871)
Progress and Trend in Novel Data Management System
CUI Bin,GAO Jun,TONG Yong-Xin,XU Jian-Qiu,ZHANG Dong-Xiang,ZOU Lei
(School of Electronics Engineering and Computer Science, Peking University, Beijing 100871, China;State Key Laboratory of Software Development Environment(Beijing University of Aeronautics and Astronautics), Beijing 100083, China;College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China;College of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China;Institute of Computer Science and Technology, Peking University, Beijing 100871, China)
Chart / table
Similar Articles
Article :Browse 983   Download 602
Received:July 03, 2018    Revised:August 21, 2018
> 中文摘要: 随着各类新型计算技术和新兴应用领域的浮现,传统数据库技术面临新的挑战,正在从适用常规应用的单一处理方法逐步转为面向各类特殊应用的多种数据处理方式.分析并展望了新型数据管理系统的研究进展和趋势,涵盖分布式数据库、图数据库、流数据库、时空数据库和众包数据库等多个领域.具体而言:分布式数据管理技术是支持可扩展的海量数据处理的关键技术;以社交网络为代表的大规模图结构数据的处理需求带来了图数据库技术的发展;流数据管理技术用来应对数据动态变化的管理需求;时空数据库主要用于支持移动对象管理;对多源、异构而且劣质数据源的集成需求催生出新型的众包数据库技术.最后讨论了新型数据库管理系统的未来发展趋势.
Abstract:With the emergence of novel computing techiniques and applications, the traditional database manamgement systems face challenges, and undergo significant shifts from the single data model processing to multiple data model processing. This paper presents a comphrensive survey on the recent progress and future direction in the novel data management systems, including distributed databases, graph databases, streaming databases, spatial-temporal databases, and crowdsourcing databases. Specifically, the distributed techinqiues play a key role to improve the scabablity of large scale data processing. Graph data management techniques are driven by the big graph management requirement in applications like social network. Stream data management techiniques are also developed to process dynamic data. Spatial-temporal databases are mainly applied in the management of mobile objects. Last but not least, the processing of multiple sources, hetergonenous and low quality data motivates the advance of crowd-sourcing techniques. This study also surveys other hot research directions and foresees the future work.
文章编号:     中图分类号:    文献标志码:
基金项目:国家自然科学基金(61832001,61572040,61822201,61622201,61602087) 国家自然科学基金(61832001,61572040,61822201,61622201,61602087)
Foundation items:National Natural Science Foundation of China (61832001, 61572040, 61822201, 61622201, 61602087)
Reference text:


CUI Bin,GAO Jun,TONG Yong-Xin,XU Jian-Qiu,ZHANG Dong-Xiang,ZOU Lei.Progress and Trend in Novel Data Management System.Journal of Software,2019,30(1):164-193