Academic Talent Model Based on Human Resource Data Mart

Download Full Text
Author(s):
Mahani Saron, Zulaiha Ali Othman
Published Date:
September 05, 2012
Issue:
Volume 2, Issue 5
Page(s):
29 - 35
DOI:
10.7815/ijorcs.25.2012.045
Views:
6229
Downloads:
430

Keywords:
academician database, classification, data mart, talent, forecasting.
Citation:
Mahani Saron, Zulaiha Ali Othman, "Academic Talent Model Based on Human Resource Data Mart". International Journal of Research in Computer Science, 2 (5): pp. 29-35, September 2012. doi:10.7815/ijorcs.25.2012.045 Other Formats

Abstract

In higher education such as university, academic is becoming major asset. The performance of academic has become a yardstick of university performance. Therefore it's important to know the talent of academicians in their university, so that the management can plan for enhancing the academic talent using human resource data. Therefore, this research aims to develop an academic talent model using data mining based on several related human resource systems. In the case study, we used 7 human resource systems in one of Government Universities in Malaysia. This study shows how automated human talent data mart is developed to get the most important attributes of academic talent from 15 different tables like demographic data, publications, supervision, conferences, research, and others. Apart from the talent attribute collected, the forecasting talent academician model developed using the classification technique involving 14 classification algorithm in the experiment for example J48, Random Forest, BayesNet, Multilayer perceptron, JRip and others. Several experiments are conducted to get the highest accuracy by applying discretization process, dividing the data set in the different interval year (1,2,3,4, no interval) and also changing the number of classes from 24 to 6 and 4. The best model is obtained 87.47% accuracy using data set interval 4 years and 4 classes with J48 algorithm.

  1. H. H. A. Talib and K. R. Jamaludin, "Aplikasi Teknologi Maklumat (IT) Dalam Pengurusan Organisasi : Sorotan Kajian," Jurnal Teknikal dan Kajian Sosial Jilid 1, pp. 89-105, 2003.
  2. M. Armstrong, "A Handbook of Human Resource Management Practice 10th Edition," pp. 1-957, 2006.
  3. A. Mehta, "Human Capital Management: A Comprehensive Approach to Augment Organizational Performance," Review of Management, Vol. 1, No. 2, April-June 2011 ISSN: 2231-0487, vol. 1, pp. 44-57, 2011.
  4. P. M. Powell, et al., "Talent Management in the NHS Managerial Workforce," National Institute for Health Research (NIHR), pp. 1-216, 2012.
  5. B. Davies and B. J. Davies, "Talent management in academies," International Journal of Educational Management, vol. 24, pp. 418 - 426, 2010. doi:10.1108/09513541080000452
  6. T. Perrin, "Talent Management: The State of the Art," A TP Track Research Report, pp. 1-17, 2005.
  7. Q. Shi and M. Chen, "Design and Development of Management System for Reserve Talents of Volleyball Athletes," in Multimedia and Information Technology (MMIT), 2010 Second International Conference on, 2010, pp. 151-154.
  8. L. Shi and Q. Bai, "Design a New Coherent Framework for Human Resource Personnel Evaluation Information System Based on Tasks Management," International Conference on Business Computing and Global Informatization, pp. 479-481, 2011. doi:10.1109/BCGIn.2011.126
  9. C. F. Chien and L. F. Chen, "Data mining to improve personnel selection and enhance human capital: A case study in high-technology industry," Expert Systems with Applications, vol. 34, pp. 280-290, 2008. doi:10.1016/j.eswa.2006.09.003
  10. H. Jantan, et al., "Classification and Prediction of Academic Talent Using Data Mining Techniques Knowledge-Based and Intelligent Information and Engineering Systems." vol. 6276, R. Setchi, et al., Eds., ed: Springer Berlin / Heidelberg, 2010, pp. 491-500.
  11. C. Mulin and H. Reen, "Arkadin develops employee talent through e-learning," Strategic HR Review, vol. 9, pp. 11 - 16, 2010.
  12. H. Jantan, "Framework of Intelligent Decision Support System for Talent Management," p. 286, 2011.
  13. C. Chen-Fu and C. Li-Fei, "Using Rough Set Theory to Recruit and Retain High-Potential Talents for Semiconductor Manufacturing," Semiconductor Manufacturing, IEEE Transactions on, vol. 20, pp. 528-541, 2007.
  14. V. Mohanraj, et al., "Intelligent Agent Based Talent Evaluation Engine Using a Knowledge Base," in Advances in Recent Technologies in Communication and Computing, 2009. ARTCom '09. International Conference on, 2009, pp. 257-259. doi:10.1109/ARTCom.2009.214
  15. N. Goonawardene, et al., "A neural netwerk based model for project risk and talent management," Advances in Neural Networks, LNCS 6064, Springer, pp. 532-539, 2010.
  16. S. Qing and C. Mengzhong, "Design and Development of Management System for Reserve Talents of Volleyball Athletes," Second International Conference on MultiMedia and Information Technology, pp. 151-154, 2010. doi:10.1109/MMIT.2010.59
  17. F. Piazza and S. Strohmeier, "Domain-Driven Data Mining in Human Resource Management: A Review," in Data Mining Workshops (ICDMW), 2011 IEEE 11th International Conference on, 2011, pp. 458-465. doi:10.1109/ICDMW.2011.68
  18. Y. Peng, "The decision tree classification and its application research in personnel management," in Electronics and Optoelectronics (ICEOE), 2011 International Conference on, 2011, pp. V1-372-V1-375.
  19. R. Wirth and J. Hipp, "CRISP-DM: Towards a standard process model for data mining," Proceedings of the Fourth International Conference on the Practical Application of Knowledge Discovery and Data Mining, pp. 29--39, 2000.
  20. X. Chen, et al., "A survey of open source data mining systems," presented at the Proceedings of the 2007 international conference on Emerging technologies in knowledge discovery and data mining, Nanjing, China, 2007.

    Sorry, there are no citation(s) for this manuscript yet.