Development of Data Warehouse for Financial Report in Faculty of Science, Naresuan University

Main Article Content

Kittipong Songsiri
Kreangsak Tamee

Abstract

Data warehouse technology is useful for managing large data. The purpose of them are analyzing organization’s datasets to formulate a strategy and direction of the organization. In this paper have present method of the development of data warehouse based on Ralph Kimball's methodology, starting from the study of the data source. Process of Extraction, Transformation and Loading (ETL) until we finished to get for summary financial data warehouse of Faculty of Science, Naresuan University. That they have problem about reporting from OLTP database that use for large data of financial management system. We using the data warehouse which is an OLAP database instead report function to analyze and follow up on the disbursement process more conveniently. In the future, data warehouse technology can also be used to analyze data in other fields within the Faculty of Science as well.

Article Details

How to Cite
Songsiri, K., & Tamee, K. (2022). Development of Data Warehouse for Financial Report in Faculty of Science, Naresuan University. Journal of Applied Informatics and Technology, 4(2), 99–113. https://doi.org/10.14456/jait.2022.8
Section
Research Article

References

งานนโยบายและแผน คณะวิทยาศาสตร์ มหาวิทยาลัยนเรศวร. (2563). รายงานผลการปฏิบัติงานของคณบดีคณะวิทยาศาสตร์ มหาวิทยาลัยนเรศวร ครบรอบปีที่ 1 วาระที่ 1. คณะวิทยาศาสตร์ มหาวิทยาลัยนเรศวร พิษณุโลก

AliEl-Sappagh, S.H., Hendawi, A.M.A., & Bastawissy, A.H. (2011). A proposed model for data warehouse ETL processes. Journal of King Saud University - Computer and Information Sciences, 23(2), 91-104. https://doi.org/10.1016/j.jksuci.2011.05.005

Bhatnagar, D., & Urolagin, S. (2021). Data warehousing for formula one (racing) popularity rating using Pentaho tools. In 6th International Conference on Computing, Communication and Automation (ICCA) (pp. 1-7). IEEE.

Gupta, A., Sahayadhas, A., & Gupta, V. (2020). Proposed techniques to design speed efficient data warehouse architecture for fastening knowledge discovery process. In Third International Conference on Artificial Intelligence and Knowledge Engineering (AIKE) (pp. 200-201). IEEE.

Harvy, I., Matitaputty, G. A., Girsang, A. S., Michael, S., & Isa, S. M. (2019). The use of book store GIS data warehouse in implementing the analysis of most book selling. In 7th International Conference on Cyber and IT Service Management (CITSM) (pp. 1-5). IEEE. https://doi.org/10.1109/CITSM47753.2019.8965404

Hassan, C.A.U., Hammad, M., Uddin, M., Iqbal, J., Sahi, J., Hussian, S., & Ullah, S.S. (2022). Optimizing the performance of data warehouse by query cache mechanism. IEEE Access, 10, 13472-13480.

Kimball, R., Ross, M., Thornthwaite, W., Mundy, J., & Becker, B. (2008). The data warehouse lifecycle toolkit. (2nd Edition). Wiely.

Kryeziu, N., Ismaili, F., Ajdari, J., Raufi, B., & Zenuni, X. (2019). Energy provider data warehouse design and implementation - Case study. In International Conference on Information Technologies (InfoTech) (pp. 1-5). IEEE. https://doi.org/10.1109/InfoTech.2019.8860876.

Ramadhani, P.P., Hadi, S., & Rosadi, R. (2021). Implementation of data warehouse in making business intelligence dashboard development using PostgreSQL database and Kimball lifecycle method. In International Conference on Artificial Intelligence and Big Data Analytics (pp. 88-92). IEEE.

Vincentdo, V., Pratama, A.R., Girsang, A.S., Suwandi, R., & Andrean, Y.P. (2019). Reporting and decision support using data warehouse for E-commerce top-up cell-phone credit transaction. In 7th International Conference on Cyber and IT Service Management (CITSM) (pp. 1-4). IEEE. https://doi.org/10.1109/CITSM47753.2019.8965349

Wang, D., Li, Q., Xu, C., Wang, P., & Wang, Z. (2021). Research of data warehouse for science and technology management system. In International Conference on Service Science (ICSS) (pp. 65-69). IEEE. https://doi.org/10.1109/ICSS53362.2021.00018