American Journal of Mathematical and Computer Modelling

Submit a Manuscript

Publishing with us to make your research visible to the widest possible audience.

Propose a Special Issue

Building a community of authors and readers to discuss the latest research and develop new ideas.

Building a Secured Data Warehouse for a University Staff Management System: A Case Study of Gombe State University, Gombe

Universities are among the several organizations with complex activities regarding staff records, payroll, and staff promotion. This work intends to use Data warehouse as a solution to simplify these complex activities. Due to their ability to combine heterogeneous data from several information sources in a single storage location for querying and analysis, data warehousing is gaining importance in terms of strategic decision making throughout time. Data collection is a long-standing practice among organizations. Building a massive data warehouse enables them store all important and relevant information. This information is available, but very few organizations have been able to use it to make informed decisions. Due to the significant role played by the Data Warehouse (DW) and Data Mining in strategic decision making, this work developed a Data Warehouse system that can be used academic staff promotion in a University. The prototype which demonstrated the benefits of Data Warehouse and sensitizes universities in Nigeria to start binding such facilities into their staff management system for the purpose of establishing effective administrative system. The system was implemented under oracle Data Warehouse Builder and is meant to serve as a repository of data for data mining operations. The system offers high degree of accuracy in predicting the case of promotion, staff-students ratio as well as the budget projection.

Data Warehouse, Oracle, Management System

APA Style

Muhammed Kabir Ahmed, Ahmadu Bappah Muhammad, Abubakar Adamu, Aishatu Yahaya Umar. (2023). Building a Secured Data Warehouse for a University Staff Management System: A Case Study of Gombe State University, Gombe. American Journal of Mathematical and Computer Modelling, 7(4), 55-60. https://doi.org/10.11648/j.ajmcm.20220704.12

ACS Style

Muhammed Kabir Ahmed; Ahmadu Bappah Muhammad; Abubakar Adamu; Aishatu Yahaya Umar. Building a Secured Data Warehouse for a University Staff Management System: A Case Study of Gombe State University, Gombe. Am. J. Math. Comput. Model. 2023, 7(4), 55-60. doi: 10.11648/j.ajmcm.20220704.12

AMA Style

Muhammed Kabir Ahmed, Ahmadu Bappah Muhammad, Abubakar Adamu, Aishatu Yahaya Umar. Building a Secured Data Warehouse for a University Staff Management System: A Case Study of Gombe State University, Gombe. Am J Math Comput Model. 2023;7(4):55-60. doi: 10.11648/j.ajmcm.20220704.12

Copyright © 2022 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

1. Akintola, K. G., Adetunmbi, A. O., & Adeola, O. S. (2011). Building Warehouse and Data Mining from Course Management System; A Case Study of FUTA Course Management Information System. International Journal of Database Theory and Application, 13-20.
2. Sherenaz, A. B., Alesso, M., & Mauro, M. (2005). Anormaly Detection in Computing Network. A State of the Art Review. Journal of Wireless Mobile Network Ubiquitors Computing and Dependable Application, 29-64.
3. Nafeez, A. F., & Rikita, M. (2018). Design of Data Warehouse for Medical Information System Using Data Mining Techniques. 5th IEEE International Conference on Parallel Distributed and Grid Computing (PDGC) (pp. 20-22). Solan, India: Solan Computing Press.
4. Majid, A., Dmitriy, D., Brihat, S., Xiaoyuan, C., Jason, B., Steven, B., Ron, P. (2019). Development and Application of a High throughput Natural Language Processing Architecture to convert all Clinical Data Warehouse into Standardized Medical Vocabularies. Journal of the American Informatics Association, 1364-1369.
5. Jie, L., Wei, Y., Wan, Z., Xinyu, Y., Hanlin, Z., & Wei, Z. (2017). A Survey on Internet of things: Architecture, Enabling Technologies Security and Privacy and Applications. Internet of Things Journal IEEE, 1-17.
6. Pravin, C., & Manoj, K. G. (2018). Comprehensive Survey on Data Warehousing Research. International Journal of Technology, 217-224.
7. Senda, B., Ahlem, N., & Faiez, G. (2019). Design a Data Warehouse Scheme from Document Oriented Database. 23rd International Conference on Knowledge Based and Intelligent Information and Engineering Systems. Procedia Computer Science. Elsevier, Science Direct.
8. Azman, T., Mohamad, S. A., Suwannit, C. C., & Mohd, H. M. (2017). Data Warehouse System for Blended Learning in Institutions of Higher Education. e-academia Journal, 144-155.
9. Antti, L., Juha-pekka, J., Mikko, R., Tommi, M., & Timo, L. (2017). Migrating from a centralized Data Warehouse to a Decentralized Data Platform Architecture. Retrieved from http://www.solita.com
10. Youssef, B. (2012). A Data Warehouse Design for a Typical University Information System. Retrieved from Lebanese Association for computational Sciences: http://www.lacsc.org
11. Quafafou, M., Naouali, G., & Nachouki, G. (2005). Knowledge Data Warehouse: Web Usage OLAP Application. International Conference on Web Intelligence, (pp. 19-22).
12. Pant, S., & Hsu, C. (1995). Information Resource Management Association. International Conference, (pp. 21-24). Georgia, Atlanta.
13. Xingquan, Z., & Ian, D. (2007). Knowledge Discovery and Data Mining. Global Research Collection.
14. Singh, Y., & Chauchan, A. S. (2013). Application of Data Mining Using Artificial Neural NetworkSurvey. International Journal of Database Theory and Application.
15. Earlvon, F. L., John, K. J., Mark, J. K., & Pagatpat, D. D. (2018). Development of a University Financial Data Warehouse and its Visualization Tool. 3rd International Conference on Computer Science and Computational Intelligence (pp. 587-595). Elsevier, Science Direct.
16. Kefi, H. and Koppel, N. (2011) ‘Measuring data warehousing success: an empirical investigation applying the DeLone and McLean model’, Int. J. Data Analysis Techniques and Strategies, Vol. 3, No. 2, pp. 178–201.