Mapreduce for big data analysis: Benefits, limitations and extensions

Yang Song; Hongzhi Wang; Jianzhong Li; Hong Gao

doi:10.1007/978-981-10-2053-7_40

Mapreduce for big data analysis: Benefits, limitations and extensions

Yang Song
, Hongzhi Wang^*
, Jianzhong Li
, Hong Gao

^*Corresponding author for this work

Faculty of Computing

Harbin Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

7 Scopus citations

Abstract

Big data becomes a hot topic. Map Reduce is a popular programming paradigm for big data analysis with many benefits. Even though it has widely applications in industry, Map Reduce still has limitations in some applications. For these limitations, some extensions have been proposed. In these brief communications, we discuss the benefits and limitations of Map Reduce programming paradigm and also its extensions to make Map Reduce go beyond the limitations.

Original language	English
Title of host publication	Social Computing - 2nd International Conference of Young Computer Scientists, Engineers and Educators, ICYCSEE 2016, Proceedings
Editors	Wanxiang Che, Hongzhi Wang, Shaoliang Peng, Weipeng Jing, Guanglu Sun, Xianhua Song, Zeguang Lu, Qilong Han, Junyu Lin, Hongtao Song
Publisher	Springer Verlag
Pages	453-457
Number of pages	5
ISBN (Print)	9789811020520
DOIs	https://doi.org/10.1007/978-981-10-2053-7_40
State	Published - 2016
Event	2nd International Conference on Young Computer Scientists, Engineers and Educators, ICYCSEE 2016 - Harbin, China Duration: 20 Aug 2016 → 22 Aug 2016

Publication series

Name	Communications in Computer and Information Science
Volume	623
ISSN (Print)	1865-0929

Conference

Conference	2nd International Conference on Young Computer Scientists, Engineers and Educators, ICYCSEE 2016
Country/Territory	China
City	Harbin
Period	20/08/16 → 22/08/16

Keywords

Analysis
Big data
Map Reduce
Parallel computation

Access to Document

10.1007/978-981-10-2053-7_40

Cite this

Song, Y., Wang, H., Li, J., & Gao, H. (2016). Mapreduce for big data analysis: Benefits, limitations and extensions. In W. Che, H. Wang, S. Peng, W. Jing, G. Sun, X. Song, Z. Lu, Q. Han, J. Lin, & H. Song (Eds.), Social Computing - 2nd International Conference of Young Computer Scientists, Engineers and Educators, ICYCSEE 2016, Proceedings (pp. 453-457). (Communications in Computer and Information Science; Vol. 623). Springer Verlag. https://doi.org/10.1007/978-981-10-2053-7_40

Song, Yang ; Wang, Hongzhi ; Li, Jianzhong et al. / Mapreduce for big data analysis : Benefits, limitations and extensions. Social Computing - 2nd International Conference of Young Computer Scientists, Engineers and Educators, ICYCSEE 2016, Proceedings. editor / Wanxiang Che ; Hongzhi Wang ; Shaoliang Peng ; Weipeng Jing ; Guanglu Sun ; Xianhua Song ; Zeguang Lu ; Qilong Han ; Junyu Lin ; Hongtao Song. Springer Verlag, 2016. pp. 453-457 (Communications in Computer and Information Science).

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title = "Mapreduce for big data analysis: Benefits, limitations and extensions",

abstract = "Big data becomes a hot topic. Map Reduce is a popular programming paradigm for big data analysis with many benefits. Even though it has widely applications in industry, Map Reduce still has limitations in some applications. For these limitations, some extensions have been proposed. In these brief communications, we discuss the benefits and limitations of Map Reduce programming paradigm and also its extensions to make Map Reduce go beyond the limitations.",

keywords = "Analysis, Big data, Map Reduce, Parallel computation",

author = "Yang Song and Hongzhi Wang and Jianzhong Li and Hong Gao",

note = "Publisher Copyright: {\textcopyright} Springer Science+Business Media Singapore 2016.; 2nd International Conference on Young Computer Scientists, Engineers and Educators, ICYCSEE 2016 ; Conference date: 20-08-2016 Through 22-08-2016",

year = "2016",

doi = "10.1007/978-981-10-2053-7\_40",

language = "英语",

isbn = "9789811020520",

series = "Communications in Computer and Information Science",

publisher = "Springer Verlag",

pages = "453--457",

editor = "Wanxiang Che and Hongzhi Wang and Shaoliang Peng and Weipeng Jing and Guanglu Sun and Xianhua Song and Zeguang Lu and Qilong Han and Junyu Lin and Hongtao Song",

booktitle = "Social Computing - 2nd International Conference of Young Computer Scientists, Engineers and Educators, ICYCSEE 2016, Proceedings",

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Song, Y, Wang, H, Li, J & Gao, H 2016, Mapreduce for big data analysis: Benefits, limitations and extensions. in W Che, H Wang, S Peng, W Jing, G Sun, X Song, Z Lu, Q Han, J Lin & H Song (eds), Social Computing - 2nd International Conference of Young Computer Scientists, Engineers and Educators, ICYCSEE 2016, Proceedings. Communications in Computer and Information Science, vol. 623, Springer Verlag, pp. 453-457, 2nd International Conference on Young Computer Scientists, Engineers and Educators, ICYCSEE 2016, Harbin, China, 20/08/16. https://doi.org/10.1007/978-981-10-2053-7_40

Mapreduce for big data analysis: Benefits, limitations and extensions. / Song, Yang; Wang, Hongzhi; Li, Jianzhong et al.
Social Computing - 2nd International Conference of Young Computer Scientists, Engineers and Educators, ICYCSEE 2016, Proceedings. ed. / Wanxiang Che; Hongzhi Wang; Shaoliang Peng; Weipeng Jing; Guanglu Sun; Xianhua Song; Zeguang Lu; Qilong Han; Junyu Lin; Hongtao Song. Springer Verlag, 2016. p. 453-457 (Communications in Computer and Information Science; Vol. 623).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Mapreduce for big data analysis

T2 - 2nd International Conference on Young Computer Scientists, Engineers and Educators, ICYCSEE 2016

AU - Song, Yang

AU - Wang, Hongzhi

AU - Li, Jianzhong

AU - Gao, Hong

N1 - Publisher Copyright: © Springer Science+Business Media Singapore 2016.

PY - 2016

Y1 - 2016

N2 - Big data becomes a hot topic. Map Reduce is a popular programming paradigm for big data analysis with many benefits. Even though it has widely applications in industry, Map Reduce still has limitations in some applications. For these limitations, some extensions have been proposed. In these brief communications, we discuss the benefits and limitations of Map Reduce programming paradigm and also its extensions to make Map Reduce go beyond the limitations.

AB - Big data becomes a hot topic. Map Reduce is a popular programming paradigm for big data analysis with many benefits. Even though it has widely applications in industry, Map Reduce still has limitations in some applications. For these limitations, some extensions have been proposed. In these brief communications, we discuss the benefits and limitations of Map Reduce programming paradigm and also its extensions to make Map Reduce go beyond the limitations.

KW - Analysis

KW - Big data

KW - Map Reduce

KW - Parallel computation

UR - https://www.scopus.com/pages/publications/84981263385

U2 - 10.1007/978-981-10-2053-7_40

DO - 10.1007/978-981-10-2053-7_40

M3 - 会议稿件

AN - SCOPUS:84981263385

SN - 9789811020520

T3 - Communications in Computer and Information Science

SP - 453

EP - 457

BT - Social Computing - 2nd International Conference of Young Computer Scientists, Engineers and Educators, ICYCSEE 2016, Proceedings

A2 - Che, Wanxiang

A2 - Wang, Hongzhi

A2 - Peng, Shaoliang

A2 - Jing, Weipeng

A2 - Sun, Guanglu

A2 - Song, Xianhua

A2 - Lu, Zeguang

A2 - Han, Qilong

A2 - Lin, Junyu

A2 - Song, Hongtao

PB - Springer Verlag

Y2 - 20 August 2016 through 22 August 2016

ER -

Song Y, Wang H, Li J, Gao H. Mapreduce for big data analysis: Benefits, limitations and extensions. In Che W, Wang H, Peng S, Jing W, Sun G, Song X, Lu Z, Han Q, Lin J, Song H, editors, Social Computing - 2nd International Conference of Young Computer Scientists, Engineers and Educators, ICYCSEE 2016, Proceedings. Springer Verlag. 2016. p. 453-457. (Communications in Computer and Information Science). doi: 10.1007/978-981-10-2053-7_40

Mapreduce for big data analysis: Benefits, limitations and extensions

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