Journal of Korean Society of Rural Planning (농촌계획)

Korean Society of Rural Planning (한국농촌계획학회)
권호 표지

Construction of Integrated Agricultural Statistical System Architecture for Effective Policy

농업정책 실효성 증대를 위한 농업통계시스템 아키텍처 구축

  • Lee, Min-Soo (Dept. of Agricultural Economics and Rural Development, Seoul Notional University) ;
  • Chae, Young-Chan (Dept. of Agricultural Economics and Rural Development, Seoul Notional University) ;
  • Hong, Hee-Yeon (Dept. of Agricultural Economics and Rural Development, Seoul Notional University) ;
  • Kim, Sang-Ho (Dept. of Agricultural Economics and Rural Development, Seoul Notional University) ;
  • Kim, Jeong-Seop (Dept. of Agricultural Economics and Rural Development, Seoul Notional University)
  • 이민수 (서울대학교 지역사회개발전공) ;
  • 최영찬 (서울대학교 지역사회개발전공) ;
  • 홍희연 (서울대학교 지역사회개발전공) ;
  • 최상호 (서울대학교 지역사회개발전공) ;
  • 김정섭 (서울대학교 지역사회개발전공)
  • Published : 2005.12.25
Download PDF
⟨ Previous Next ⟩

Abstract

This study designs an integrated data architecture to systematically manage the agricultural statistics database. Managing the agricultural statistics is important since it provides data for policies and decision making for agribusinesses. Ministry of Agriculture and the National Statistical Office collect the basic agricultural statistic data which provides the basis of logical decision making and agricultural policies. However, the agricultural statistic data has not well been used. The data has not been consistently collected nor managed. The raw data has not been organized nor processed to meet various demands. The needs has been arisen for a consistent agricultural statistics system to increase the relevance, accessibility, and efficiency of data for various users. There are massive amount of data accumulated over a long time period. Introducing the new system and reorganizing the data will bear large risks. A systematic method is required to reduce the risks in planing, building, and maintaining the database without hindering administration. This study provides a design of the agricultural statistics system architecture based on the user requirement analysis (URA) and similar systems abroad. We have also build a prototype to check the implementability of the system design.

Keywords

References

  1. Cabena, P, P. hadjinian, R. Stadler, J. Verhees, and A. Zanasi, 1998, Discovering Data Mining: From Concept to Implementation. Prentice Hall, New Jersey
  2. Chen, M.S., J. Jan, and P.S. Yu, 1996, 'Data Mining: An Overview from a Database Perspective', IEEE Transactions on Knowledge and Data Engineering 8(6) : 866-883 https://doi.org/10.1109/69.553155
  3. Eckerson, W.W., 1988, 'Post-Chasm Warehousing', Journal of Data Warehousing 3(3): 38-45
  4. Eckerson, W.W., 1999, Evolution of Data Warehousing: The Trend toward Analytical Applications, Boston, MA: The Patricia Seybold Group, 1-8
  5. Eckerson, W.W. and H.J. Watson, 2000, Harnessing Customer Information for Strategic Advantage: Technical Challenges and Business Solutions. Seattle: The Data Warehousing Institute
  6. EDS, 1995, 'Data Warehouseing Primer', EDS, December
  7. Fayyad, U., G. Piatetsky-Shapiro, and P. Smyth, 1996, 'The KDD Process for Extracting Useful Knowledge from Volumes of Data', Communication of the ACM 39(11) : 27-34 https://doi.org/10.1145/240455.240464
  8. Gray, P. and H.J. Watson, 1998, Decision Support in the Data Warehouse, Upper Saddle River, Prentice-Hall, New Jersey
  9. Griffin, J., 1995, 'Customer Information Architecture', DBMS: 58-65
  10. Han, J. and M. Kamber, 2001, Data Mining: Concepts and Technique, Morgan-Kaufmann Academic Press, San Francisco
  11. Inmon, W.H., 2001, 'Knowing Your Dss End User: Tourists, Explorers, Farmers', www.billinmon.com/library/articles
  12. Inmon, W.H., 2002, Building the Data Warehouse. John Wiley and Sons, New York
  13. Legg, W., 2005, 'Agricultural Support Indicators: Measurement, Meaning and Use', In Staistics, Knowledge and Policy(pp. 635-644), OECD(Eds), http://www.sourceoecd.org/9264009000/ (accessed 2005.12)
  14. Markus, M.L., 2001, 'Towards a Theory of Knowledge Reuse: Types of Knowledge Reuse Situations and Factors in Reuse Success,' Journal of Management Information Systems 18(1) : 57-93 https://doi.org/10.1201/1078/43196.18.3.20010601/31291.8
  15. McFadden, F.R., 1996, 'Data Warehouse for EIS: Some Issues and Impacts', Proceedings of 29th Annual Hawaii International Conference on System Science : 120-129
  16. OECD, 2003, Methodology for the Measurement of Support and Use in Policy Evaluation, http://www.oecd.org/agr.support (accessed 2005.12)
  17. Pendse, N., 2003, 'What is OLAP, The OLAP Report', (http://www.olapreport.com/FASMI.HTM;White Paper)
  18. Poe, V., 1995, 'Data Warehouse: Architecture is not Infrastructure', Database Programming and Design
  19. Stephens, T.R., 2004, 'Knowledge: The Essence of Meta Data: The Meta Data Experience', DM Review 14(3)
  20. Umar, 1993, Distributed Computing and Client/Server Systems. Prentice Hall
  21. USDA, 2005, USDA Strategic Plan for FY 2002-2007, http://www.usda.gov/ocfo/usdasp/usdasp.htm(accessed 2005.12)
  22. Vaduva, A. and T. Vetterli, 2001, 'Metadata Management for Data Warehousing: An Overview', International Journal of Cooperative Information Systems 10(3) : 273-298 https://doi.org/10.1142/S0218843001000357
  23. Watson, H.J., 2001, 'Recent Developments in Data Warehosing', Communications of the Association for Information Systems 8 : 1-25
  24. Werner, V. and C. Abramson, 2001, 'Managing Click-steam Data', Journal of Data Warehousing 6(3) : 11-15
  25. Yost, M., 2000, 'Data warehousing and decision support at the National Agricultural Statistics Service,' Social Science Computer Review 18(4) : 434-441 https://doi.org/10.1177/089443930001800406