DOI QR코드

DOI QR Code

Experiencing with Splunk, a Platform for Analyzing Machine Data, for Improving Recruitment Support Services in WorldJob+

머신 데이터 분석용 플랫폼 스플렁크를 이용한 취업지원 서비스 개선에 관한 연구 : 월드잡플러스 사례를 중심으로

  • Lee, Jae Deug (Information Support Bureau, Human Resources Development Service of Korea) ;
  • Rhee, MoonKi Kyle (School of Business, SungKyunKwan University) ;
  • Kim, Mi Ryang (Dept. of Computer Education, SungKyunKwan University)
  • 이재덕 (한국산업인력공단 정보화지원국) ;
  • 이문기 (성균관대학교 경영대학) ;
  • 김미량 (성균관대학교 컴퓨터교육과)
  • Received : 2018.01.10
  • Accepted : 2018.03.20
  • Published : 2018.03.28

Abstract

WorldJob+, being operated by The Human Resources Development Service of Korea, provides a recruitment support services to overseas companies wanting to hire talented Korean applicants and interns, and support the entire course from overseas advancement information check to enrollment, interview, and learning for young job-seekers. More than 300,000 young people have registered in WorldJob+, an overseas united information network, for job placement. To innovate WorldJob+'s services for young job-seekers, Splunk, a powerful platform for analyzing machine data, was introduced to collate and view system log files collected from its website. Leveraging Splunk's built-in data visualization and analytical features, WorldJob+ has built custom tools to gain insight into the operation of the recruitment supporting service system and to increase its integrity. Use cases include descriptive and predictive analytics for matching up services to allow employers and job seekers to be matched based on their respective needs and profiles, and connect jobseekers with the best recruiters and employers on the market, helping job seekers secure the best jobs fast. This paper will cover the numerous ways WorldJob+ has leveraged Splunk to improve its recruitment supporting services.

한국산업인력공단이 운영하는 월드잡플러스는 청년들의 해외취업을 지원하는 포털 서비스로서 해외진출에 필요한 정보제공과 등록, 면접, 학습 등 일련의 과정을 지원하는 통합정보네트워크이다. 현재 30만명 이상의 청년들이 등록하고 있으며, 연계관련기관과 협업하여 청년들의 해외 취업을 지원한다. 월드잡플러스는 지원서비스의 혁신화와 무결성 유지를 위해 머신데이터 분석플랫폼인 스플렁크를 활용하여 웹사이트에 축적된 로그파일 분석을 시도하고 있다. 기술적 예측적 분석도구를 이용하여 구직자 니즈와 프로필 기반의 맞춤형 매칭 서비스를 제공하며 구직자를 위한 최적 구직 성공요건 및 최적 구인기업에 대한 정보를 제공한다. 본 논문에서는 월드잡플러스가 스플렁크를 활용하여 해외취업을 지원하는 몇 가지 서비스에 대한 사례를 제시해보고자 한다.

Keywords

References

  1. Gartner. (2017). Big data. http://blogs.gartner.com.
  2. T. U. Kim. (2016). Priniciples of Management, Seoul : SinYoungSa.
  3. S. H. Lee & D. W. Lee. (2013). Current Status Of Big Data Utilization. Journal of Digital Convergence, 11(2), 229-233. https://doi.org/10.14400/JDPM.2013.11.12.229
  4. National Informatization Strategy Committee. (2011). Smart Government with Utilization of Big data, www.nia.or.kr.
  5. M. Y. Kim & D. J. Seo. (2014). An Analysis of the Public Data for Making the Ambient Intelligent Service. Journal of Digital Convergence, 12(12), 313-321. https://doi.org/10.14400/JDC.2014.12.12.313
  6. M. Lee. (2011). Big Data and the Utilization of Public Data. Internet and Information Security, 2(2), 47-64.
  7. J. S. Han. (2014). Utilization Outlook of Medical Big Data in the Cloud Environment. Journal of Digital Convergence, 12(2), 397-407.
  8. Y. M. Lee. (2017. 4. 8). Big data can locate the welfare dead zone. E-daily, p. 12.
  9. NIA. (2015). Public data becomes the base camp for success. www.daegu.go.kr/Images/public data/NIA.
  10. P. Russom. (2011). Big Data Analytics. TDWI Best Pratices Report, 1-34.
  11. B. Hazen, J. B. Skipper, J. D. Ezell & C. A. Boone. (2016). Big Data and predictive analytics for supply chain sustainability: A theory-driven research agenda. Computers & Industrial Engineering, 101, 592-598. https://doi.org/10.1016/j.cie.2016.06.030
  12. Y. K. Jung, M. Suk & C. Kim. (2014). A Study on the Success Factors of Big Data through analysis of Introduction Effect of Big Data. Journal of Digital Convergence, 12(11), 241-248. https://doi.org/10.14400/JDC.2014.12.11.241
  13. J. Huh. (2017). Crime Prevention with Big Data Analysis. Donga Business Review, 235, 120-126.
  14. Y. Hahm. (2017). Data Integration Strategy in Big Data Era: A Public Sector Case Analysis. Journal of Information Technology and Architecture, 14(2), 115-128.
  15. S. Kim, H. Shin & S. Son. (2014). A Study on Large-Scale Traffic Information Modeling using R. Journal of KIISE : System and Theory, 41(4), 151-157.
  16. B. Y. Lee, J. T. Lim & J. Yoo. (2013). Utilization of Social Media Analysis using Big Data. Journal of the Korea Contents Assoication, 13(2), 211-219.
  17. https://www.worldjob.or.kr/ovsea/
  18. http://www.hancommds.com/splunk/
  19. B. C. Kim. (2013). Big Data Security Technology and Response Study. Journal of Digital Convergence, 11(10), 445-451. https://doi.org/10.14400/JDPM.2013.11.10.445
  20. J. Zikic and A.M. Saks. (2009). Job Search and Social Cognitive Theory: The Role of Career-relevant Activities. Journal of Vocational Behavior, 74(1), 117-127. https://doi.org/10.1016/j.jvb.2008.11.001
  21. A. Tziner, E. Vered & L. Ophir. (2004). Predictors of job search intensity among college graduates. Journal of Career Assessment, 12(3), 332-344. https://doi.org/10.1177/1069072704266677