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首页 > 过刊浏览>2019年第30卷第1期 >164-193. DOI:10.13328/j.cnki.jos.005646
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新型数据管理系统研究进展与趋势
作者:
作者简介:

崔斌(1975-),男,浙江宁波人,博士,教授,博士生导师,CCF杰出会员,主要研究领域为数据库系统,大数据管理和分析;高军(1975-),男,博士,教授,博士生导师, CCF高级会员,主要研究领域为分布式数据管理,图数据管理和深度分析;童咏昕(1982-),男,博士,副教授,CCF专业会员,主要研究领域为众包数据管理,群体智能,时空数据管理与挖掘,不确定数据管理与挖掘;许建秋(1982-),男,博士,副教授,CCF专业会员,主要研究领域为空间,移动对象数据管理;张东祥(1985-),男,博士,教授,CCF专业会员,主要研究领域为时空大数据分析,智能交通优化;邹磊(1981-),男,博士,教授,博士生导师,CCF高级会员,主要研究领域为图数据库系统,知识图谱分析.

通讯作者:

崔斌,E-mail:bin.cui@pku.edu.cn

基金项目:

国家自然科学基金（61832001，61572040，61822201，61622201，61602087）


Progress and Trend in Novel Data Management System
Author:
Fund Project:

National Natural Science Foundation of China (61832001, 61572040, 61822201, 61622201, 61602087)

  • 摘要
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  • 访问统计
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  • 参考文献 [185]
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  • 相似文献 [20]
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  • 文章评论
    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.

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崔斌,高军,童咏昕,许建秋,张东祥,邹磊.新型数据管理系统研究进展与趋势.软件学报,2019,30(1):164-193

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