Parallel Processing Systems for Big Data: A Survey
文献类型：期刊论文
| 作者 | Zhang, Yunquan2; Cao, Ting2; Li, Shigang2; Tian, Xinhui3; Yuan, Liang2; Jia, Haipeng2; Vasilakos, Athanasios V.1 |
| 刊名 | PROCEEDINGS OF THE IEEE
 |
| 出版日期 | 2016-11-01 |
| 卷号 | 104期号:11页码:2114-2136 |
| 关键词 | Big data machine learning MapReduce parallel processing SQL survey |
| ISSN号 | 0018-9219 |
| DOI | 10.1109/JPROC.2016.2591592 |
| 英文摘要 | 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. |
| 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 |
| 语种 | 英语 |
| WOS记录号 | WOS:000386244000005 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/8031]  |
| 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.Lulea Univ Technol, Dept Comp Sci Elect & Space Engn, S-97187 Lulea, Sweden 2.Chinese Acad Sci, Inst Comp Technol, State Key Lab Comp Architecture, Beijing 100190, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, Adv Comp Syst Res Ctr, Beijing 100190, Peoples R China |
| 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. |
入库方式： OAI收割
来源：计算技术研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。