Analysis of the Job Categories of the New Japanese Information Technology Skills Standards

  • Rasha El-Agamy
  • Kazuhiko Tsuda
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 246)

Abstract

Organizations operate in an increasingly competitive environment, which drives a need for continuous employee skill development. The rapid pace of technological change requires everyone to engage in life-long learning. The importance of the information technology services industry is growing year by year, and it would not be exaggerating to say that it is playing a major role in placecountry-regionJapan’s industry. So, the Japanese government has published the documents that define the required knowledge about IT. These documents are called the Information Technology Skill Standards (ITSS) . The ITSS documents define 11 job categories and 35 special fields. In order to learn efficiently, it is indispensable to discern what is important for targeted for learning. This paper analyzes the Japanese skill standards using text mining methods. These methods were used to extract the keywords and to compute the similarity between the different job categories of skill standards. This type of analysis has not made intensively, such as clustering the skill standards’ job categories and the required skills to change engineer‘s career. For these backgrounds, the authors made an intensive research with the eleven job categories of the Japanese information technology skill standards published by the Japanese ministry of economy, trade and industry. From the results of the research, the authors have succeeded in proposing a method that enables the engineers to identify the required keywords to move from job category to another. Also, high weighted keywords were used to sort the required learning courses for any job category. The authors think that this method will make it easy to the engineers to know the priority of the required learning courses in every job category.

Keywords

Information Technology Job Categories Clustering Cosine Similarity Learning Skill Standards 

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References

  1. 1.
    Evans, N.: Information technology jobs and skill standards. In: Hawkins, B.L., Rudy, J.A., Wallace, W.H. (eds.) Technology Everywhere: A Campus Agenda for Educating and Managing Workers in the Digital Age, pp. 25–38. Jossey-Bass, A Wiley Company (2002)Google Scholar
  2. 2.
    Broderick, R.F., Boudreau, J.W.: Human resource management, information technology, and the competitive edge. Academy of Management Executive 6(2), 7–17 (1992)Google Scholar
  3. 3.
    Gardner, S.D., Lepak, D.P., Bartol, K.M.: Virtual hr: The impact of information technology on the human resource professional. Journal of Vocational Behavior 63, 159–179 (2003)CrossRefGoogle Scholar
  4. 4.
    Manning, C.D., Raghavan, P., Schutze, H.: An Introduction to Information Retrieval. Cambridge University Press (2009)Google Scholar
  5. 5.
    Salton, G., McGill, M.: Introduction to modern information retrieval. McGraw-Hill, New York (1983)MATHGoogle Scholar
  6. 6.
    Atlam, E.S.: A new approach for text similarity using articles. Int. J. Information Technology and Decision Making 7(6), 23–34 (2008)CrossRefMATHGoogle Scholar
  7. 7.
    Deisy, C., Gowri, M., Baskar, S., Kalaiarasi, S., Ramraj, N.: A novel term weighting scheme midf for text categorization. Journal of Engineering Science and Technology 5(1), 94–107 (2010)Google Scholar
  8. 8.
    Salton, G.M., Yang, C.: On the specification of term values in automatic indexing. Journal of Documentation 29(4), 351–372 (1973)CrossRefGoogle Scholar
  9. 9.
    Salton, G.M., Buckley, C.: Term-weighting approaches in automatic text retrieval. Information Processing&Management 24, 513–523 (1988)CrossRefGoogle Scholar
  10. 10.
    Salton, G.M., Buckley, C.: Term-weighting approaches in automatic text retrieval. Information Processing&Management 24, 513–523 (1988)CrossRefGoogle Scholar
  11. 11.
    Salton, G.M., Wong, A., Yang, C.: A vector space model for automatic indexing. Communications of the ACM 18, 613–620, ISSN: 0001-0782 EISSN: 1557-7317Google Scholar
  12. 12.
    Atlam, E.S., Fuketa, M., Morita, K., Ichi Aoe, J.: Documents dissimilarity measurement using field association terms. Information Processing and Management 39(6), 809–824 (2003)CrossRefGoogle Scholar
  13. 13.
    Gupta, V., Lehal, G.S.: Features selection and weight learning for punjabi text summarization. International Journal of Engineering Trends and Technology (2011)Google Scholar
  14. 14.
    Huang, A.: Similarity measures for text document clustering. In: Computer Science Research Student Conference, New Zealand, Christchurch (2008)Google Scholar
  15. 15.
    Salton, G.M.: Automatic text processing. Addison-Wesley Longman Publishing Co., Inc., Boston (1988) ISBN:0-2:1-1227-8Google Scholar
  16. 16.
    Veni, R.: Effects of similarity metrics on document clustering. Master’s thesis, (School of Computer Science Howard R. Hughes College of Engineering) (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Rasha El-Agamy
    • 1
    • 2
  • Kazuhiko Tsuda
    • 1
    • 2
  1. 1.Department of Risk Engineering Faculty of Systems and Information EngineeringUniversity of TsukubaJapan
  2. 2.Graduate School of Business SciencesUniversity of TsukubaBunkyoJapan

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