research-article

WebMapReduce: an accessible and adaptable tool for teaching map-reduce computing

Authors:

Patrick Garrity,

Timothy Yates,

Richard Brown,

Elizabeth ShoopAuthors Info & Claims

SIGCSE '11: Proceedings of the 42nd ACM technical symposium on Computer science education

Pages 183 - 188

https://doi.org/10.1145/1953163.1953221

Published: 09 March 2011 Publication History

Get Access

Abstract

WebMapReduce (WMR) is a strategically simplified user interface for the Hadoop implementation of the map-reduce model for distributed computing on clusters, designed so that novice programmers in an introductory CS courses can perform authentic data-intensive scalable computations using the programming language they are learning in their course. The open-source WMR software currently supports Java, C++, Python, and Scheme computations, and can readily be extended to support additional programming languages, and configured to adapt to the practices at a particular institution for teaching introductory programming. Potential applications in courses at all undergraduate levels are indicated, and implementation of the WMR software is described.

References

[1]

Amazon Elastic Compute Cloud (Amazon EC2). http://aws.amazon.com/ec2/. Accessed: 09--10--2010.

Google Scholar

[2]

Brin, S. and Page, L. 1998. The anatomy of a large-scale hypertextual Web search engine. Computer Networks and ISDN Systems. 30, 1--7 (1998), 107--117.

Digital Library

Google Scholar

[3]

Concurrency and Map-Reduce Strategies in Various Programming Languages. http://serc.carleton.edu/csinparallel/modules/concurrency_pl.html. Accessed: 09--10--2010.

Google Scholar

[4]

Data.gov. http://www.data.gov/. Accessed: 09--10--2010.

Google Scholar

[5]

Dean, J. and Ghemawat, S. 2004. MapReduce: Simplified Data Processing on Large Clusters. Operating System Design and Implementation (San Francisco, CA, December 2004), 137--150.

Digital Library

Google Scholar

[6]

Hadoop. http://hadoop.apache.org/. Accessed: 09--10--2010.

Google Scholar

[7]

Hadoop Streaming. http://hadoop.apache.org/common/docs/r0.20.1/streaming.html. Accessed: 09--10--2010.

Google Scholar

[8]

Infochimps Data Marketplace. http://infochimps.org/. Accessed: 09--10--2010.

Google Scholar

[9]

Johnson, M. et al. 2008. Infusing Parallelism into Introductory Computer Science Curriculum using MapReduce. EECS Department, University of California, Berkeley.

Google Scholar

[10]

Map-reduce Computing using WebMapReduce. http://serc.carleton.edu/csinparallel/modules/IntroWMR.html. Accessed: 09--10--2010.

Google Scholar

[11]

Project Gutenberg. http://www.gutenberg.org/wiki/Main_Page. Accessed: 09--10--2010.

Google Scholar

[12]

Second Interpreter of Scheme Code. http://sisc-scheme.org/. Accessed: 09--10--2010.

Google Scholar

[13]

STk. http://kaolin.unice.fr/STk/. Accessed: 09--10--2010.

Google Scholar

[14]

WebMapReduce. http://webmapreduce.sourceforge.net/. Accessed: 09--10--2010.

Google Scholar

Cited By

View all

Wu WStephenson BStone JBattestilli LRebelsky SShoop L(2024)Learning Big Data Systems via EmulationProceedings of the 55th ACM Technical Symposium on Computer Science Education V. 110.1145/3626252.3630888(1449-1455)Online publication date: 7-Mar-2024
https://dl.acm.org/doi/10.1145/3626252.3630888
Bunde DAhmed KAyloo SBrown-Gaines TFuentes JJatala VKurniawati ROz IQasem ASchielke PTedeschi MYeh T(2022)Adopting Heterogeneous Computing Modules: Experiences from a ToUCH Summer Workshop2022 IEEE/ACM International Workshop on Education for High Performance Computing (EduHPC)10.1109/EduHPC56719.2022.00008(18-25)Online publication date: Nov-2022
https://doi.org/10.1109/EduHPC56719.2022.00008
Yang ZGuo X(2020)Teaching Hadoop Using Role Play GamesDecision Sciences Journal of Innovative Education10.1111/dsji.1219718:1(6-21)Online publication date: 8-Feb-2020
https://doi.org/10.1111/dsji.12197
Show More Cited By

Index Terms

WebMapReduce: an accessible and adaptable tool for teaching map-reduce computing

Recommendations

CSinParallel: using map-reduce to teach parallel programming concepts across the CS curriculum (abstract only)
SIGCSE '13: Proceeding of the 44th ACM technical symposium on Computer science education

Map-reduce, the cornerstone computational framework for cloud computing applications, has star appeal to draw students to the study of parallelism. Participants will carry out hands-on exercises designed for students at CS1/intermediate/advanced levels ...
CSinParallel: Using WebMapReduce to Teach Parallel Computing Concepts, Hands-on (Abstract Only)
SIGCSE '16: Proceedings of the 47th ACM Technical Symposium on Computing Science Education

Map-reduce computation is the on-ramp to data-intensive cloud computing, and arguably the most widely deployed form of parallel/distributed computing. Participants will carry out exercises designed for students at CS1, intermediate, and advanced levels ...
Sharing incremental approaches for adding parallelism to CS curricula (abstract only)
SIGCSE '12: Proceedings of the 43rd ACM technical symposium on Computer Science Education

Recent industry changes, including multi-core processors, cloud computing, and GPU programming, increase the need to teach parallelism to CS undergraduates. But few CS programs can afford to add new courses or greatly alter syllabi, and the large ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

SIGCSE '11: Proceedings of the 42nd ACM technical symposium on Computer science education

March 2011

754 pages

ISBN:9781450305006

DOI:10.1145/1953163

General Chairs:
Thomas J. Cortina
Carnegie Mellon University
,
Ellen L. Walker
Hiram College
,
Program Chairs:
Laurie Smith King
College of the Holy Cross
,
David R. Musicant
Carleton College

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 March 2011

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

SIGCSE '11

Sponsor:

SIGCSE

SIGCSE '11: The 42nd ACM Technical Symposium on Computer Science Education

March 9 - 12, 2011

TX, Dallas, USA

Acceptance Rates

SIGCSE '11 Paper Acceptance Rate 107 of 315 submissions, 34%;

Overall Acceptance Rate 1,595 of 4,542 submissions, 35%

Upcoming Conference

SIGCSE TS 2025

Sponsor:
sigcse

The 56th ACM Technical Symposium on Computer Science Education

February 26 - March 1, 2025

Pittsburgh , PA , USA

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

26
Total Citations
View Citations
303
Total Downloads

Downloads (Last 12 months)3
Downloads (Last 6 weeks)0

Reflects downloads up to 17 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Wu WStephenson BStone JBattestilli LRebelsky SShoop L(2024)Learning Big Data Systems via EmulationProceedings of the 55th ACM Technical Symposium on Computer Science Education V. 110.1145/3626252.3630888(1449-1455)Online publication date: 7-Mar-2024
https://dl.acm.org/doi/10.1145/3626252.3630888
Bunde DAhmed KAyloo SBrown-Gaines TFuentes JJatala VKurniawati ROz IQasem ASchielke PTedeschi MYeh T(2022)Adopting Heterogeneous Computing Modules: Experiences from a ToUCH Summer Workshop2022 IEEE/ACM International Workshop on Education for High Performance Computing (EduHPC)10.1109/EduHPC56719.2022.00008(18-25)Online publication date: Nov-2022
https://doi.org/10.1109/EduHPC56719.2022.00008
Yang ZGuo X(2020)Teaching Hadoop Using Role Play GamesDecision Sciences Journal of Innovative Education10.1111/dsji.1219718:1(6-21)Online publication date: 8-Feb-2020
https://doi.org/10.1111/dsji.12197
Watkinson NTai PNicolau AVeidenbaum A(2020)NumbaSummarizer: A Python Library for Simplified Vectorization Reports2020 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)10.1109/IPDPSW50202.2020.00058(1-7)Online publication date: May-2020
https://doi.org/10.1109/IPDPSW50202.2020.00058
Becker BQuille KHawthorne EPérez-Quiñones MHeckman SZhang J(2019)50 Years of CS1 at SIGCSEProceedings of the 50th ACM Technical Symposium on Computer Science Education10.1145/3287324.3287432(338-344)Online publication date: 22-Feb-2019
https://dl.acm.org/doi/10.1145/3287324.3287432
Ngo LSrinath ADenton JZiolkowski M(2018)Unifying computing resources and access interface to support parallel and distributed computing educationJournal of Parallel and Distributed Computing10.1016/j.jpdc.2018.02.020118:P1(201-212)Online publication date: 1-Aug-2018
https://dl.acm.org/doi/10.1016/j.jpdc.2018.02.020
Ferraro Petrillo U(2018)Using Software Visualization for Supporting the Teaching of MapReduceNetwork and System Security10.1007/978-3-030-02744-5_26(349-360)Online publication date: 18-Dec-2018
https://doi.org/10.1007/978-3-030-02744-5_26
Matthews S(2017)Using Phoenix++ MapReduce to introduce undergraduate students to parallel computingJournal of Computing Sciences in Colleges10.5555/3069658.306968232:6(165-174)Online publication date: 1-Jun-2017
https://dl.acm.org/doi/10.5555/3069658.3069682
Bogaerts S(2017)One step at a timeJournal of Parallel and Distributed Computing10.1016/j.jpdc.2016.12.024105:C(4-17)Online publication date: 1-Jul-2017
https://dl.acm.org/doi/10.1016/j.jpdc.2016.12.024
Sukhoroslov O(2017)A Service-Oriented Infrastructure for Teaching Big Data TechnologiesSupercomputing10.1007/978-3-319-71255-0_41(505-515)Online publication date: 15-Nov-2017
https://doi.org/10.1007/978-3-319-71255-0_41
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

CSinParallel: using map-reduce to teach parallel programming concepts across the CS curriculum (abstract only)

CSinParallel: Using WebMapReduce to Teach Parallel Computing Concepts, Hands-on (Abstract Only)

Sharing incremental approaches for adding parallelism to CS curricula (abstract only)