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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.

Keywords

Big-data analytics;WorldJob+;Splunk;Recruitment service;Visualization

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. https://doi.org/10.14400/JDC.2014.12.2.397
  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