한국융합학회논문지 (Journal of the Korea Convergence Society)

  • 제12권7호
  • /
  • Pages.45-51
  • /
  • 2021
  • /
  • 2233-4890(pISSN)
  • /
  • 2713-6353(eISSN)
한국융합학회 (Korea Convergence Society)
권호 표지
DOI QR코드

DOI QR Code

빅데이터 수집 처리를 위한 분산 하둡 풀스택 플랫폼의 설계

Design of Distributed Hadoop Full Stack Platform for Big Data Collection and Processing

  • 이명호 (세명대학교 정보통신학부)
  • Lee, Myeong-Ho (School of Information Communication, Semyung University)
  • 투고 : 2021.04.10
  • 심사 : 2021.07.20
  • 발행 : 2021.07.28
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

급속한 비대면 환경과 모바일 우선 전략에 따라 해마다 많은 정형/비정형 데이터의 폭발적인 증가와 생성은 모든 분야에서 빅데이터를 활용한 새로운 의사 결정과 서비스를 요구하고 있다. 그러나 매년 급속히 증가하는 빅데이터를 활용하여 실무 환경에서 적용 가능한 표준 플랫폼으로 빅데이터를 수집하여 적재한 후, 정재한 빅데이터를 관계형 데이터베이스에 저장하고 처리하는 하둡 에코시스템 활용의 참조 사례들은 거의 없었다. 따라서 본 연구에서는 스프링 프레임워크 환경에서 3대의 가상 머신 서버를 통하여 하둡 2.0을 기반으로 쇼셜 네트워크 서비스에서 키워드로 검색한 비정형 데이터를 수집한 후, 수집된 비정형 데이터를 하둡 분산 파일 시스템과 HBase에 적재하고, 적재된 비정형 데이터를 기반으로 형태소 분석기를 이용하여 정형화된 빅데이터를 관계형 데이터베이스에 저장할 수 있게 설계하고 구현하였다. 향후에는 데이터 심화 분석을 위한 하이브나 머하웃을 이용하여 머신 러닝을 이용한 클러스터링과 분류 및 분석 작업 연구가 지속되어야 할 것이다.

In accordance with the rapid non-face-to-face environment and mobile first strategy, the explosive increase and creation of many structured/unstructured data every year demands new decision making and services using big data in all fields. However, there have been few reference cases of using the Hadoop Ecosystem, which uses the rapidly increasing big data every year to collect and load big data into a standard platform that can be applied in a practical environment, and then store and process well-established big data in a relational database. Therefore, in this study, after collecting unstructured data searched by keywords from social network services based on Hadoop 2.0 through three virtual machine servers in the Spring Framework environment, the collected unstructured data is loaded into Hadoop Distributed File System and HBase based on the loaded unstructured data, it was designed and implemented to store standardized big data in a relational database using a morpheme analyzer. In the future, research on clustering and classification and analysis using machine learning using Hive or Mahout for deep data analysis should be continued.

키워드

과제정보

This study has been supported by Semyung University Research Year 2020.

참고문헌

  1. IDC. (2020. May). Worldwide Global DataSphere Forecast. Global DataSphere. https://www.idc.com/getdoc.jsp?containerId=prUS46286020
  2. Domo. (2020. August). Data Never Sleeps 8.0. https://www.domo.com/news/press/domo-releases-eighth-annual-data-never-sleeps-infographic
  3. Lori Lewis. (2020). This Is What Happens In An Internet Minute. https://lorilewismedia.com/
  4. visually. (2020). This Is What Happens In An Internet Minute. https://visual.ly/community/Infographics/other/what-happens-internet-minute-2020
  5. J. H. Park, S. Y. Lee, D. H. Kang & J. H. Won. (2013). Hadop and MapReduce. Journal of Korea Data & Information Science Society, 24(5), 1013-1027. https://doi.org/10.7465/jkdi.2013.24.5.1013
  6. S. B. Heo, D. C. Kang & J. Y. Choi. (2019). Hadoop based Deep Learning Framework Technology Trend. Communications of the Korean Institute of Information Scientists and Engineers, 37(10), 25-31.
  7. Apache Hadoop 2.10.1, https://hadoop.apache.org/docs/r2.10.1/
  8. Apache Hadoop 3.1.4, https://hadoop.apache.org/docs/r3.1.4/
  9. National Information Society Agency. eGovFrame. https://www.egovframe.go.kr/home/sub.do?menuNo=32
  10. S. B. Heo, D. C. Kang & J. Y. Choi. (2019). Technology Trends of Deep Learning Framework on Hadoop YARN. Communications of the Korean Institute of Information Scientists and Engineers, 37(10), 25-31.
  11. H. J. Kim. (2015). Design and Implementation of an Efficient Web Services Data Processing Using Hadoop-Based Big Data Processing, Journal of the Korea Academia-Industrial cooperation Society, 16(1), 726-734. DOI : 10.5762/kais.2015.16.1.726
  12. Wikipedia. Apache Flume. https://en.wikipedia.org/wiki/Apache_Flume
  13. Wikipedia. Apache Kafka. https://en.wikipedia.org/wiki/Apache_Kafka
  14. Wikipedia. Apache Storm. https://en.wikipedia.org/wiki/Apache_Storm
  15. Wikipedia. Esper. https://en.wikipedia.org/wiki/Esper_(software)
  16. Apache HBase. https://hbase.apache.org/
  17. Wikipedia. Redis. https://en.wikipedia.org/wiki/Redis
  18. Wikipedia.. Apache Spark. https://en.wikipedia.org/wiki/Apache_Spark
  19. Wikipedia. Apache Hive. https://en.wikipedia.org/wiki/Apache_Hive
  20. Wikipedia. Apache Oozie. https://en.wikipedia.org/wiki/Apache_Oozie
  21. Apache Airflow, https://airflow.apache.org/
  22. Wikipedia. Apache Sqoop. https://en.wikipedia.org/wiki/Sqoop
  23. Wikipedia. Machine Learning. https://en.wikipedia.org/wiki/Machine_learning
  24. Wikipedia. Apache Mahout. https://en.wikipedia.org/wiki/Machine_learning
  25. Wikipedia. Apache Impala. https://en.wikipedia.org/wiki/Apache_Impala
  26. Apache Zookeeper. http://zookeeper.apache.org/
  27. Data Flair. https://data-flair.training/blogs/hadoop-2-vshadoop-3/
  28. Wikipedia. Spring Framework. https://en.wikipedia.org/wiki/Spring_Framework
  29. I. M. Lee. (2012). Spring 3.1 of Toby(Vol. 1). Seoul : Acorn.
  30. MyBatis. (2021. April). https://mybatis.org/mybatis-3/index.html
  31. Wikipedia. Web scraping. https://en.wikipedia.org/wiki/Web_scraping
  32. SNU IDC. Kkma. http://kkma.snu.ac.kr/
  33. Shineware. Komoran. https://www.shineware.co.kr/products/komoran/
  34. SWRC. Han nanum. http://semanticweb.kaist.ac.kr/hannanum/
  35. Atlassian. Bitbucket, MeCab-ko. https://bitbucket.org/eunjeon/mecab-ko-dic/src/master/
  36. Twitter. Open Korea Text(Okt). https://github.com/open-korean-text/open-korean-text
  37. Kakao Tech. Khaiii. https://tech.kakao.com/2018/12/13/khaiii/
  38. Wikipedia. VirtualBox. https://www.virtualbox.org/wiki/VirtualBox
  39. National Information Society Agency. Publicdata Portal, https://www.data.go.kr/
  40. Naver Developers. Naver Open API. https://developers.naver.com/docs/search/blog/