Parallel Processing Systems for Big Data: A Survey

doi:10.1109/JPROC.2016.2591592

	Parallel Processing Systems for Big Data: A Survey
	Zhang, Yunquan 1; Cao, Ting 1; Li, Shigang 1; Tian, Xinhui 2; Yuan, Liang 1; Jia, Haipeng 1; Vasilakos, Athanasios V.3
	2016-11-01
发表期刊	PROCEEDINGS OF THE IEEE
ISSN	0018-9219
卷号	104 期号:11 页码:2114-2136
摘要	The volume, variety, and velocity properties of big data and the valuable information it contains have motivated the investigation of many new parallel data processing systems in addition to the approaches using traditional database management systems (DBMSs). MapReduce pioneered this paradigm change and rapidly became the primary big data processing system for its simplicity, scalability, and fine-grain fault tolerance. However, compared with DBMSs, MapReduce also arouses controversy in processing efficiency, low-level abstraction, and rigid dataflow. Inspired by MapReduce, nowadays the big data systems are blooming. Some of them follow MapReduce's idea, but with more flexible models for general-purpose usage. Some absorb the advantages of DBMSs with higher abstraction. There are also specific systems for certain applications, such as machine learning and stream data processing. To explore new research opportunities and assist users in selecting suitable processing systems for specific applications, this survey paper will give a high-level overview of the existing parallel data processing systems categorized by the data input as batch processing, stream processing, graph processing, and machine learning processing and introduce representative projects in each category. As the pioneer, the original MapReduce system, as well as its active variants and extensions on dataflow, data access, parameter tuning, communication, and energy optimizations will be discussed at first. System benchmarks and open issues for big data processing will also be studied in this survey.
关键词	Big data machine learning MapReduce parallel processing SQL survey
DOI	10.1109/JPROC.2016.2591592
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2016YFB0200803] ; National Natural Science Foundation of China[61432018] ; National Natural Science Foundation of China[61133005] ; National Natural Science Foundation of China[61272136] ; National Natural Science Foundation of China[61521092] ; National Natural Science Foundation of China[61502450] ; National Natural Science Foundation of China[61402441] ; National High Technology Research and Development Program of China[2015AA01A303] ; National High Technology Research and Development Program of China[2015AA011505] ; China Postdoctoral Science Foundation[2015T80139] ; Key Technology Research and Development Programs of Guangdong Province[2015B010108006] ; CAS Interdisciplinary Innovation Team of Efficient Space Weather Forecast Models
WOS研究方向	Engineering
WOS类目	Engineering, Electrical & Electronic
WOS记录号	WOS:000386244000005
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：49[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/8032
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Zhang, Yunquan
作者单位	1.Chinese Acad Sci, Inst Comp Technol, State Key Lab Comp Architecture, Beijing 100190, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Adv Comp Syst Res Ctr, Beijing 100190, Peoples R China 3.Lulea Univ Technol, Dept Comp Sci Elect & Space Engn, S-97187 Lulea, Sweden
推荐引用方式 GB/T 7714	Zhang, Yunquan,Cao, Ting,Li, Shigang,et al. Parallel Processing Systems for Big Data: A Survey[J]. PROCEEDINGS OF THE IEEE,2016,104(11):2114-2136.
APA	Zhang, Yunquan.,Cao, Ting.,Li, Shigang.,Tian, Xinhui.,Yuan, Liang.,...&Vasilakos, Athanasios V..(2016).Parallel Processing Systems for Big Data: A Survey.PROCEEDINGS OF THE IEEE,104(11),2114-2136.
MLA	Zhang, Yunquan,et al."Parallel Processing Systems for Big Data: A Survey".PROCEEDINGS OF THE IEEE 104.11(2016):2114-2136.