Textual Data Mining Applications in the Service Chain Knowledge Management of e-Government

Document Type : Research Paper

Authors

1 Associate Prof., Faculty of Engineering & Technology, University of Qom, Qom, Iran

2 MSc in Information Technology Engineering, Faculty of Technology and Engineering, University of Qom.

Abstract

Systems related to knowledge management can improve quality and efficiency of knowledge used for decision making process. Approximately 80 percent of corporate information are in textual data formats. That is why text mining is useful and important in service chain knowledge management. For example, one of the most important applications of text mining is in managing on-line source of digital documents and the analysis of internal documents. This research is based on text-based documents and textual information and interviews processed by Grounded theory. In this research clustering techniques were applied at first step. In the second step, Apriori association rules techniques for discovering and extracting the most useful association rules were applied. In other words, integration of datamining techniques was emphasized to improve the accuracy and precision of classification. Using decision tree technique for classification may result in reducing classification precision. But, the proposed method showed a significant improvement in classification precision.

Keywords

Main Subjects

References

ثقفی، ف.؛ علی احمدی، ع.؛ قاضی نوری، س.س. و حورعلی، م. (1394)، تدوین و شناسایی سناریوهای امکان‎‎پذیرآیندۀ خدمات دولت الکترونیک ایران در افق 1404. فصلنامۀ مدیریت فناوری اطلاعات، 7(1)، 68-49.‎
رضائی نور، ج.؛ لسانی، ر.؛ زکی‎زاده، ع. و صفا مجید، غ. (1393)، بررسی شبکه‎های همکاری نویسندگی در حوزۀ فناوری اطلاعات با استفاده از تکنیک‎های شبکه‎های اجتماعی، فصلنامۀ مدیریت فناوری اطلاعات،  6(2)، 250-229.
زارعی، ب.؛ ثقفی، ف. و زرین، ل. (1392)، سنجش میزان تأثیر رویکرد قابلیت بر توسعۀ دولت الکترونیکی، فصلنامۀ مدیریت فناوری اطلاعات، 5(2)، 94-75.‎
Agrawal, R., Imielinski, T. & Swami, A. (1993). Mining association rule between sets of items in large databases. In Proceedings of international conference on management of data (SIGMOD 93). (pp. 207–216).
Berry, M. J. A. & Linoff, G. (2004). Data mining techniques for marketing, sales and customer relationship management. Hoboken, NJ: Wiley Computer Publishing.
Cheung, M. S. & Myers, M. B. (2008). Managing knowledge sharing networks in global supply chains. International Journal of Management and Decision Making, 9(6), 581-599.
Han, J. & Kamber, M. (2000). Data mining: Concepts and techniques. San Francisco: Morgan Kaufmann.
Hult, G.T.M., Ketchen, D.J. & Slater, S.F. (2004). Information processing, knowledge development, and strategic supply chain performance. Academy of management journal, 47(2), 241-253.
Jafari, M., Rezaeenour, J. & Akhavan, P. (2009). Identifying progressive route of organizational knowledge creation theory. World Applied Sciences Journal, 7 (10), 1287-1294.
Khalfan, M. M., Kashyap, M., Li, X. & Abbott, C. (2010). Knowledge management in construction supply chain integration. International Journal of Networking and Virtual Organisations, 7(2-3), 207-221.
Miao, D., Duan, Q., Zhang, H. & Jiao, N. (2009). Rough set based hybrid algorithm for text classification. Expert Systems with Applications, 36(5), 9168–9174.
Myers, M. B. & Cheung, M. S. (2008). Sharing global supply chain knowledge. MIT Sloan Management Review, 49(4), 67-73.
Nasukawa, T., & Nagano, T. (2001). Text analysis and knowledge mining systems. IBM Systems Journal, 40(4), 967 - 984.
Rezaeenour, J., Lesani, R., Zakizadeh, A. & Majid, G.S. (2014). Evaluating Authorship Collaboration Networks in the Field of Information Technology Using Social Netwowk Techniques. Information Technology Management,6(2), 229-250. (in Persian)
Rezaienour, J. & Nazaridoust, M. (2012). Data Mining Application in Analysis of Knowledge Management Gaps. In Proceeding of the 2nd World Conference on Soft Computing (pp. 551-557).
Saghafi, F., Aliahmadi, A., Ghazinoory, S.S. & Hourali, M. (2015). Developing and Identifying Possibility & Plausibility of E-Government Services Scenarios in Iran by 1404, Journal of "Information Technology Management, 7(1),  49-68. (in Persian)
Sivakumar, K. & Roy, S. (2004). Knowledge redundancy in supply chains: a framework. Supply Chain Management: An International Journal, 9(3), 241-249.
Ur-Rahman, N. & Harding, J.A. (2012). Textual data mining for industrial knowledge management and text classification: A business oriented approach. Expert Systems with Applications, 39(5), 4729-4739.
Witten, I. H. & Frank, E. (2000). Data mining: Practical machine learning tools and techniques with java implementations. San Francisco: Morgan Kaufman.
Wu, D. J. (2001). Software agents for knowledge management: coordination in multi-agent supply chains and auctions. Expert Systems with Applications, 20(1), 51-64.
Zarei, B., Saghafi, F. & Zarrin, L. (2013). Measuring the Amount of Effects of Capability Approach on Developing E-government, Information Technology Management, 5(2), 75-94. (in Persian)
 
Journal of Information Technology Management
Volume 9, Issue 1
2017
Pages 39-60

Files

Share

How to cite

Statistics