Data Technology: New Interdisciplinary Science & Technology

데이터 기술: 지식창조를 위한 새로운 융합과학기술

  • Park, Sung-Hyun (Directorate for Basic Research in Science and Engineering, National Research Foundation of Korea)
  • 박성현 (한국연구재단 기초연구본부)
  • Received : 2010.07.17
  • Accepted : 2010.09.13
  • Published : 2010.09.30

Abstract

Data Technology (DT) is a new technology which deals with data collection, data analysis, information generation from data, knowledge generation from modelling and future prediction. DT is a newly emerged interdisciplinary science & technology in this 21st century knowledge society. Even though the main body of DT is applied statistics, it also contains management information system (MIS), quality management, process system analysis and so on. Therefore, it is an interdisciplinary science and technology of statistics, management science, industrial engineering, computer science and social science. In this paper, first of all, the definition of DT is given, and then the effects and the basic properties of DT, the differences between IT and DT, the 6 step process for DT application, and a DT example are provided. Finally, the relationship among DT, e-Statistics and Data Mining is explained, and the direction of DT development is proposed.

Keywords

References

  1. 박성현 (2001a), "데이터 기술의 경제학", 한국경제신문 오피니언, 2001년 12월 3일자.
  2. 박성현 (2001b), "지식기반 사회에서의 통계학 패러다임의 변화와 데이터 기술의 발전", 경영정보논총, 서울대학교 경영대학, 제11권, p.53-59, 2001. 12.
  3. Chatfield, C. (1995), "Model uncertainty, Data Mining, and statistical inference", Journal of Royal Statistical Society, Series A, Vol. 158, p. 419-466. https://doi.org/10.2307/2983440
  4. Clark, G. (1997), "Statistical Themes and Lessons for Data Mining", Data Mining and Knowledge Disco very, Vol. 1, p. 11-28. https://doi.org/10.1023/A:1009773905005
  5. Devillers,J.C.(2002),"e-Statistics for Deriving QSAR Models", SAR and QSAR in Environmental Research, Vol. 13, pp. 409-416.
  6. Erto, P., Pallotta, G. and Park, S. H. (2008), "An Example of Data Technology Product: a Control Chart for Weibull Processes", International Statistical Review, Vol. 76, No. 2, pp. 157-166. https://doi.org/10.1111/j.1751-5823.2008.00043.x
  7. Friedman,J. H. (2001),"The Role of Statistics in the Data Revolution",International Statistical Review, 69, p. 5-10. https://doi.org/10.1111/j.1751-5823.2001.tb00474.x
  8. Healy, M. J. R. (1978), "Is Statis-tics a Science?", Journal of the Royal Statistical Society, Series A, 141, p. 385-393. https://doi.org/10.2307/2344809
  9. Kohavi, R., and Provost, F. (2001), "Applications of Data Mining to Electronic Commerce", Data Mining and Knowledge Discovery, Vol. 5, p. 5-10. https://doi.org/10.1023/A:1009840925866
  10. Park,S. H. (2003), "Data Technol- ogy and Knowle dge-based Six Sigma", Asian Journal on Quality, Vol. 4, p. 40-45. https://doi.org/10.1108/15982688200300003
  11. Park, S. H. and Suh, M. W. (2008), "Data Technology as a New Discipline for Broader Application of Stati stics", Journal of Data Science, Vol. 6, No. 3, pp. 357-368, July 2008 issue, ISSN 1683-8602(on-line).
  12. Straf, M. L. (2003), "Statistics: The Next Gener ation", Journal of the American Statistical Association, 98, p. 1-6. https://doi.org/10.1198/016214503388619030