摘要
首先根据处理形式的不同,介绍了不同形式数据的特征和各自的典型应用场景以及相应的代表性处理系统,总结了大数据处理系统的三大发展趋势;随后,对系统支撑下的大数据分析技术和应用(包括深度学习、知识计算、社会计算与可视化等)进行了简要综述,总结了各种技术在大数据分析理解过程中的关键作用;最后梳理了大数据处理和分析面临的数据复杂性、计算复杂性和系统复杂性挑战,并逐一提出了可能的应对之策.
This paper first introduces the key features of big data in different processing modes and their typical application scenarios, as well as corresponding representative processing systems. It then summarizes three development trends of big data processing systems. Next, the paper gives a brief survey on system supported analytic technologies and applications （including deep learning, knowledge computing, social computing, and visualization）, and summarizes the key roles of individual technologies in big data analysis and understanding. Finally, the paper lays out three grand challenges of big data processing and analysis, i.e., data complexity, computation complexity, and system complexity. Potential ways for dealing with each complexity are also discussed.
出处
《软件学报》
EI
CSCD
北大核心
2014年第9期1889-1908,共20页
Journal of Software
基金
国家重点基础研究发展计划(973)(2014CB340401
2012CB316303)
国家自然科学基金(61232010
61100175
61173008
61202214)
北京市科技新星计划(Z121101002512063)
关键词
大数据
数据分析
深度学习
知识计算
社会计算
可视化
dig data
data analysis
deep learning
knowledge computing
social computing
visualization
