MapReduce解决方案分类及其实现

IF 1.6 Q2 EDUCATION, SCIENTIFIC DISCIPLINES International Journal of Engineering Pedagogy Pub Date : 2023-07-06 DOI:10.3991/ijep.v13i5.38867
K. Orynbekova, A. Bogdanchikov, S. Cankurt, A. Adamov, S. Kadyrov
{"title":"MapReduce解决方案分类及其实现","authors":"K. Orynbekova, A. Bogdanchikov, S. Cankurt, A. Adamov, S. Kadyrov","doi":"10.3991/ijep.v13i5.38867","DOIUrl":null,"url":null,"abstract":"Distributed Systems are widely used in industrial projects and scientific research. The Apache Hadoop environment, which works on the MapReduce paradigm, lost popularity because new, modern tools were developed. For example, Apache Spark is preferred in some cases since it uses RAM resources to hold intermediate calculations; therefore, it works faster and is easier to use. In order to take full advantage of it, users must think about the MapReduce concept. In this paper, a usual solution and MapReduce solution of ten problems were compared by their pseudocodes and categorized into five groups. According to these groups’ descriptions and pseudocodes, readers can get a concept of MapReduce without taking specific courses. This paper proposes a five-category classification methodology to help distributed-system users learn the MapReduce paradigm fast. The proposed methodology is illustrated with ten tasks. Furthermore, statistical analysis is carried out to test if the proposed classification methodology affects learner performance. The results of this study indicate that the proposed model outperforms the traditional approach with statistical significance, as evidenced by a p-value of less than 0.05. The policy implication is that educational institutions and organizations could adopt the proposed classification methodology to help learners and employees acquire the necessary knowledge and skills to use distributed systems effectively.","PeriodicalId":45481,"journal":{"name":"International Journal of Engineering Pedagogy","volume":null,"pages":null},"PeriodicalIF":1.6000,"publicationDate":"2023-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"MapReduce Solutions Classification by Their Implementation\",\"authors\":\"K. Orynbekova, A. Bogdanchikov, S. Cankurt, A. Adamov, S. Kadyrov\",\"doi\":\"10.3991/ijep.v13i5.38867\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Distributed Systems are widely used in industrial projects and scientific research. The Apache Hadoop environment, which works on the MapReduce paradigm, lost popularity because new, modern tools were developed. For example, Apache Spark is preferred in some cases since it uses RAM resources to hold intermediate calculations; therefore, it works faster and is easier to use. In order to take full advantage of it, users must think about the MapReduce concept. In this paper, a usual solution and MapReduce solution of ten problems were compared by their pseudocodes and categorized into five groups. According to these groups’ descriptions and pseudocodes, readers can get a concept of MapReduce without taking specific courses. This paper proposes a five-category classification methodology to help distributed-system users learn the MapReduce paradigm fast. The proposed methodology is illustrated with ten tasks. Furthermore, statistical analysis is carried out to test if the proposed classification methodology affects learner performance. The results of this study indicate that the proposed model outperforms the traditional approach with statistical significance, as evidenced by a p-value of less than 0.05. The policy implication is that educational institutions and organizations could adopt the proposed classification methodology to help learners and employees acquire the necessary knowledge and skills to use distributed systems effectively.\",\"PeriodicalId\":45481,\"journal\":{\"name\":\"International Journal of Engineering Pedagogy\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.6000,\"publicationDate\":\"2023-07-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Engineering Pedagogy\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3991/ijep.v13i5.38867\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"EDUCATION, SCIENTIFIC DISCIPLINES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Engineering Pedagogy","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3991/ijep.v13i5.38867","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"EDUCATION, SCIENTIFIC DISCIPLINES","Score":null,"Total":0}
引用次数: 0

摘要

分布式系统广泛应用于工业项目和科学研究中。基于MapReduce范式的ApacheHadoop环境由于开发了新的现代工具而失去了流行性。例如,Apache Spark在某些情况下是首选,因为它使用RAM资源来保存中间计算;因此,它工作速度更快,更易于使用。为了充分利用它,用户必须考虑MapReduce的概念。在本文中,通过伪代码比较了十个问题的常用解决方案和MapReduce解决方案,并将其分为五组。根据这些小组的描述和伪代码,读者可以在不参加特定课程的情况下获得MapReduce的概念。本文提出了一种五类分类方法,以帮助分布式系统用户快速学习MapReduce范式。通过十项任务说明了所提出的方法。此外,还进行了统计分析,以测试所提出的分类方法是否会影响学习者的表现。这项研究的结果表明,所提出的模型在统计学意义上优于传统方法,p值小于0.05证明了这一点。政策含义是,教育机构和组织可以采用拟议的分类方法,帮助学习者和员工获得有效使用分布式系统所需的知识和技能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
MapReduce Solutions Classification by Their Implementation
Distributed Systems are widely used in industrial projects and scientific research. The Apache Hadoop environment, which works on the MapReduce paradigm, lost popularity because new, modern tools were developed. For example, Apache Spark is preferred in some cases since it uses RAM resources to hold intermediate calculations; therefore, it works faster and is easier to use. In order to take full advantage of it, users must think about the MapReduce concept. In this paper, a usual solution and MapReduce solution of ten problems were compared by their pseudocodes and categorized into five groups. According to these groups’ descriptions and pseudocodes, readers can get a concept of MapReduce without taking specific courses. This paper proposes a five-category classification methodology to help distributed-system users learn the MapReduce paradigm fast. The proposed methodology is illustrated with ten tasks. Furthermore, statistical analysis is carried out to test if the proposed classification methodology affects learner performance. The results of this study indicate that the proposed model outperforms the traditional approach with statistical significance, as evidenced by a p-value of less than 0.05. The policy implication is that educational institutions and organizations could adopt the proposed classification methodology to help learners and employees acquire the necessary knowledge and skills to use distributed systems effectively.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
International Journal of Engineering Pedagogy
International Journal of Engineering Pedagogy EDUCATION, SCIENTIFIC DISCIPLINES-
CiteScore
5.50
自引率
35.00%
发文量
42
审稿时长
8 weeks
期刊最新文献
Virtual Laboratory Learning Environment: VLLE on Metaverse for University in Thailand MapReduce Solutions Classification by Their Implementation Implementation of New-Product Creativity through an Engineering Design Process to Foster Engineering Students’ Higher-Order Thinking Skills Contribution of Online Tutoring in Promoting the Quality of Distance Learning for Moroccan Teachers Spatial Strategies Employed by Blind and Low-Vision (BLV) Individuals on the Tactile Mental Cutting Test (TMCT)
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1