Development of a Higher Education Data Warehouse Using the Data Vault 2.0 Method

Authors

  • Bagas Triaji Universitas Teknologi Digital Indonesia
  • Aloysius Agus Subagyo Universitas Teknologi Digital Indonesia
  • Muhammad Arif Rifai Universitas Teknologi Digital Indonesia

DOI:

10.33395/sinkron.v8i4.14215

Keywords:

data vault, higher education, data warehouse, data management, information system

Abstract

In this research, we investigate the potential of Data Vault 2.0 modeling as a solution to address the complexity of data management in higher education, which is often spread across multiple information systems. The main objective of this research is to confirm the effectiveness of Data Vault 2.0 in building a data warehouse, as well as facilitating the integration of data from different sources, such as the Academic Information System, Personnel Information System, and New Student Admission System. The research method used includes data collection and processing through the staging stage before being stored in the Data Vault structure consisting of hubs, links, and satellites. The research findings show that Data Vault 2.0 not only provides flexibility in development but also allows two developers to work in parallel without interfering with each other, speeding up the data integration process. In addition, the design evaluation results show that Data Vault 2.0 is able to accommodate dynamic changes in requirements, while facilitating the creation of dashboards for data visualization and analysis. The conclusion of this research emphasizes that although Data Vault 2.0 is more complicated than models such as star schema, it provides advantages in flexibility and better data integration. Further research is needed to address the challenges of data integration and deepen the understanding of the implementation of this model in various contexts.

GS Cited Analysis

Downloads

Download data is not yet available.

References

Ansari, F., Glawar, R., & Sihn, W. (2020). Prescriptive Maintenance of CPPS by Integrating Multimodal Data with Dynamic Bayesian Networks. 1–8. https://doi.org/10.1007/978-3-662-59084-3_1

Anshari, M., Almunawar, M. N., Lim, S. A., & Al-Mudimigh, A. (2019). Customer relationship management and big data enabled: Personalization & customization of services. Applied Computing and Informatics, 15(2), 94–101. https://doi.org/10.1016/j.aci.2018.05.004

Asmita, M., Henny, H., & Samidi, S. (2023). Data Warehouse Modelling Information Security Log Management in Building a Security Operation Center in Central Government Agencies With Kimball Method. Jurnal Teknik Informatika (Jutif), 4(4), 987–994. https://doi.org/10.52436/1.jutif.2023.4.4.649

Giebler, C., Gröger, C., Hoos, E., Schwarz, H., & Mitschang, B. (2019). Modeling Data Lakes with Data Vault: Practical Experiences, Assessment, and Lessons Learned. In Journal of Clinical Microbiology (Vol. 38, Issue 1, pp. 63–77). https://doi.org/10.1007/978-3-030-33223-5_7

Joshua, S. R., & Mogea, T. (2020). Agile analytics: Adoption framework for business intelligence in higher education. Journal of Theoretical and Applied Information Technology, 98(7), 1032–1042.

Livera, A., Theristis, M., Koumpli, E., Theocharides, S., Makrides, G., Sutterlueti, J., Stein, J. S., & Georghiou, G. E. (2021). Data processing and quality verification for improved photovoltaic performance and reliability analytics. Progress in Photovoltaics: Research and Applications, 29(2), 143–158. https://doi.org/10.1002/pip.3349

Nambiar, A., & Mundra, D. (2022). An Overview of Data Warehouse and Data Lake in Modern Enterprise Data Management. Big Data and Cognitive Computing, 6(4), 132. https://doi.org/10.3390/bdcc6040132

Nayak, I., & Teixeira, F. L. (2022). Data-Driven Modeling of High-Q Cavity Fields Using Dynamic Mode Decomposition. 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (AP-S/URSI), 9(2), 1118–1119. https://doi.org/10.1109/AP-S/USNC-URSI47032.2022.9886176

Oliveira, Ó., & Oliveira, B. (2022). An Extensible Framework for Data Reliability Assessment. International Conference on Enterprise Information Systems, ICEIS - Proceedings, 1(Iceis), 77–84. https://doi.org/10.5220/0010863600003179

Ouafiq, E. M., Saadane, R., Chehri, A., & Jeon, S. (2022). AI-based modeling and data-driven evaluation for smart farming-oriented big data architecture using IoT with energy harvesting capabilities. Sustainable Energy Technologies and Assessments, 52, 102093. https://doi.org/10.1016/j.seta.2022.102093

Passi, A., Tibocha-Bonilla, J. D., Kumar, M., Tec-Campos, D., Zengler, K., & Zuniga, C. (2021). Genome-Scale Metabolic Modeling Enables In-Depth Understanding of Big Data. Metabolites, 12(1), 14. https://doi.org/10.3390/metabo12010014

Peng, Z., Feng, X., Liu, M., Yang, Y., Su, H., Xie, H., Liang, Y., & Li, Y. (2022). Metadata Versioning of Data Vault Data Warehouse. 2022 IEEE/CIC International Conference on Communications in China (ICCC Workshops), 3(2), 188–193. https://doi.org/10.1109/ICCCWorkshops55477.2022.9896652

S. Elsheikh, A. (2022). Blockchain Analytics Reference Architecture for FinTech - A Positioning Paper. Federated Africa and Middle East Conference on Software Engineering, 1–7. https://doi.org/10.1145/3531056.3531068

Sais, M., Rafalia, N., & Abouchabaka, J. (2022). Enhancements and an Intelligent Approach To Optimize Big Data Storage and Management: Random Enhanced Hdfs (Rehdfs) and Dna Storage. International Journal on Technical and Physical Problems of Engineering, 14(1), 196–203.

Sarwar, M. I., Iqbal, M. W., Alyas, T., Namoun, A., Alrehaili, A., Tufail, A., & Tabassum, N. (2021). Data Vaults for Blockchain-Empowered Accounting Information Systems. IEEE Access, 9, 117306–117324. https://doi.org/10.1109/ACCESS.2021.3107484

Sequeira, R., Reis, A., Alves, P., & Branco, F. (2024). Roadmap for Implementing Business Intelligence Systems in Higher Education Institutions: Systematic Literature Review. Information, 15(4), 208. https://doi.org/10.3390/info15040208

Spits Warnars, H. L. H., Warnars, L. S., Ramadhan, A., Siswanto, T., & Doucet, A. (2024). Data Warehouse Design for Firefighters Operational at the DKI Jakarta Fire Department. TEM Journal, 381(9870), 365–376. https://doi.org/10.18421/TEM131-38

Urbinati, A., Bogers, M., Chiesa, V., & Frattini, F. (2019). Creating and capturing value from Big Data: A multiple-case study analysis of provider companies. Technovation, 84–85(May), 21–36. https://doi.org/10.1016/j.technovation.2018.07.004

Uzun-Per, M., Can, A. B., Volkan Gurel, A., & Aktas, M. S. (2021). Big Data Testing Framework for Recommendation Systems in e-Science and e-Commerce Domains. 2021 IEEE International Conference on Big Data (Big Data), 2353–2361. https://doi.org/10.1109/BigData52589.2021.9672082

Wang, D., Li, Q., Xu, C., Wang, P., & Wang, Z. (2021). Research of Data Warehouse for Science and Technology Management System. 2021 International Conference on Service Science (ICSS), 65–69. https://doi.org/10.1109/ICSS53362.2021.00018

Downloads


Crossmark Updates

How to Cite

Triaji, B. ., Subagyo, A. A. ., & Rifai, M. A. . (2024). Development of a Higher Education Data Warehouse Using the Data Vault 2.0 Method. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 8(4), 2591-2602. https://doi.org/10.33395/sinkron.v8i4.14215