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.
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References
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)
Broderick, R.F., Boudreau, J.W.: Human resource management, information technology, and the competitive edge. Academy of Management Executive 6(2), 7–17 (1992)
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)
Manning, C.D., Raghavan, P., Schutze, H.: An Introduction to Information Retrieval. Cambridge University Press (2009)
Salton, G., McGill, M.: Introduction to modern information retrieval. McGraw-Hill, New York (1983)
Atlam, E.S.: A new approach for text similarity using articles. Int. J. Information Technology and Decision Making 7(6), 23–34 (2008)
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)
Salton, G.M., Yang, C.: On the specification of term values in automatic indexing. Journal of Documentation 29(4), 351–372 (1973)
Salton, G.M., Buckley, C.: Term-weighting approaches in automatic text retrieval. Information Processing&Management 24, 513–523 (1988)
Salton, G.M., Buckley, C.: Term-weighting approaches in automatic text retrieval. Information Processing&Management 24, 513–523 (1988)
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: 1557-7317
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)
Gupta, V., Lehal, G.S.: Features selection and weight learning for punjabi text summarization. International Journal of Engineering Trends and Technology (2011)
Huang, A.: Similarity measures for text document clustering. In: Computer Science Research Student Conference, New Zealand, Christchurch (2008)
Salton, G.M.: Automatic text processing. Addison-Wesley Longman Publishing Co., Inc., Boston (1988) ISBN:0-2:1-1227-8
Veni, R.: Effects of similarity metrics on document clustering. Master’s thesis, (School of Computer Science Howard R. Hughes College of Engineering) (2009)
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El-Agamy, R., Tsuda, K. (2013). Analysis of the Job Categories of the New Japanese Information Technology Skills Standards. In: Tweedale, J.W., Jain, L.C. (eds) Advanced Techniques for Knowledge Engineering and Innovative Applications. Communications in Computer and Information Science, vol 246. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42017-7_19
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DOI: https://doi.org/10.1007/978-3-642-42017-7_19
Publisher Name: Springer, Berlin, Heidelberg
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