Academic Data Warehouse Modeling in Higher Education Using Nine-Step Design Methodology

  • Denny Jean Cross Sihombing Atma Jaya Catholic University of Indonesia, Indonesia
Keywords: Data Warehouse, Star Schema, Nine Step Methodology

Abstract

Data and information are essential in various fields today, as well as in the field of education, especially in universities. Some universities already have information systems that support data and information needs. However, the system has not been integrated, so it cannot provide data and information needs quickly and in an integrated manner. Information systems in universities are still primarily departmental because each was built at a different time and uses another platform. The departmental nature of this information system causes inaccuracies and inconsistencies of data that drive the information produced in reports and data reused in transactions to be invalid. Invalid data, in the end, also impacts decision-making taken by management. This study aims to develop a data warehouse at a university to integrate academic data using a star schema. The method used is the Nine Step Methodology. The result of this research is data warehouse architecture used in the academic field; fact tables and ERDs have been designed at the current stage of designing a Prototype of the Study Program Performance Sheet (LKPS).

Downloads

Download data is not yet available.

References

L. W. Santoso and Yulia, “Data Warehouse with Big Data Technology for Higher Education,” in Procedia Computer Science, 2017, vol. 124, pp. 93–99. doi: 10.1016/j.procs.2017.12.134.

S. Bouaziz, A. Nabli, and F. Gargouri, “Design a data warehouse schema from document-oriented database,” Procedia Comput Sci, vol. 159, pp. 221–230, 2019, doi: 10.1016/j.procs.2019.09.177.

M. Mirzaei, N. Zaerpour, and R. de Koster, “The impact of integrated cluster-based storage allocation on parts-to-picker warehouse performance,” Transp Res E Logist Transp Rev, vol. 146, Feb. 2021, doi: 10.1016/j.tre.2020.102207.

M. AlMeghari, S. Taha, H. Elmahdy, and X. Shen, “A proposed authentication and group-key distribution model for data warehouse signature, DWS framework,” Egyptian Informatics Journal, no. xxxx, 2020, doi: 10.1016/j.eij.2020.09.002.

A. Filiana, A. G. Prabawati, M. N. A. Rini, G. Virginia, and B. Susanto, “Perancangan Data Warehouse Perguruan Tinggi untuk Kinerja Penelitian dan Pengabdian kepada Masyarakat,” Jurnal Teknik Informatika dan Sistem Informasi, vol. 6, no. 2, pp. 174–183, 2020, doi: 10.28932/jutisi.v6i2.2557.

M. Souibgui, F. Atigui, S. Zammali, S. Cherfi, and S. ben Yahia, “Data quality in ETL process: A preliminary study,” Procedia Comput Sci, vol. 159, pp. 676–687, 2019, doi: 10.1016/j.procs.2019.09.223.

V. Khatibi, A. Keramati, and F. Shirazi, “Deployment of a business intelligence model to evaluate Iranian national higher education,” Social Sciences & Humanities Open, vol. 2, no. 1, p. 100056, 2020, doi: 10.1016/j.ssaho.2020.100056.

A. Cuzzocrea, “SpPolap: Computing privacy-preserving OLAP data cubes effectively and efficiently algorithms, complexity analysis and experimental evaluation,” Procedia Comput Sci, vol. 176, pp. 3831–3842, 2020, doi: 10.1016/j.procs.2020.09.337.

D. Nurmalasari, D. H. Qudsi, M. S. Zulvi, and W. Nengsih, “Pemodelan Data dengan Skema Galaksi pada Data Lulusan,” pp. 123–129, 2020.

O. A. Omitaomu et al., “A new methodological framework for hazard detection models in health information technology systems,” J Biomed Inform, vol. 124, Dec. 2021, doi: 10.1016/j.jbi.2021.103937.

R. Kimball, M. Ross, and A. A. Anisimov, The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling (2nd Edition), vol. 32, no. 3. 2003. doi: 10.1145/945721.945741.

L. W. Santoso and Yulia, “Data Warehouse with Big Data Technology for Higher Education,” Procedia Comput Sci, vol. 124, pp. 93–99, 2017, doi: 10.1016/j.procs.2017.12.134.

R. Martins, M. T. Pereira, L. P. Ferreira, J. C. Sá, and F. J. G. Silva, “Warehouse operations logistics improvement in a cork stopper factory,” in Procedia Manufacturing, 2020, vol. 51, pp. 1723–1729. doi: 10.1016/j.promfg.2020.10.240.

S. W. Y. Cheng, K. L. Choy, and H. Y. Lam, “A workflow decision support system for achieving customer satisfaction in warehouses serving machinery industry,” in IFAC-PapersOnLine, May 2015, vol. 28, no. 3, pp. 1714–1719. doi: 10.1016/j.ifacol.2015.06.333.

R. K. Singh, N. Chaudhary, and N. Saxena, “Selection of warehouse location for a global supply chain: A case study,” IIMB Management Review, vol. 30, no. 4, pp. 343–356, Dec. 2018, doi: 10.1016/j.iimb.2018.08.009.

Published
2022-12-03
Abstract views: 298 times
Download PDF: 169 times
How to Cite
Sihombing, D. (2022). Academic Data Warehouse Modeling in Higher Education Using Nine-Step Design Methodology. Journal of Information Systems and Informatics, 4(4), 1126-1134. https://doi.org/10.51519/journalisi.v4i4.399