AI First Framework for Predictive Database Replatforming

Authors

  • Rahul Jain

Keywords:

Replatforming, AI, Database, Predictive Analytics

Abstract

The paper proposes the AI First Framework of Predictive Database Replatforming, which focuses on integrating the gradient boosting, graph neural networks and optimization models. The quantitative analysis was carried out on 480 workloads of enterprises to assess the results of costs, risk, and migration. The findings indicate that the AI-based system will be 45 percent more accurate in predicting ROI, cut down on downtimes by 40 percent, and will be more cost-effective by 25 percent than the conventional system. The framework is a methodical means of planning complicated database migrations, through predictive intelligence, which has quantifiable business worth, reduced operational risk, and quicker conversion to contemporary cloud-based database frameworks.

Downloads

Download data is not yet available.

References

Castro-Martín, L., Del Mar Rueda, M., Ferri-García, R., & Hernando-Tamayo, C. (2021). On the Use of Gradient Boosting Methods to Improve the Estimation with Data Obtained with Self-Selection Procedures. Mathematics, 9(23), 2991. https://doi.org/10.3390/math9232991

Jin, M., Koh, H. Y., Wen, Q., Zambon, D., Alippi, C., Webb, G., I., King, I., & Pan, S. (2023). A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2307.03759

Sivaraju, P. S. (2025). Phased Enterprise Data Migration Strategies: Achieving regulatory compliance in wholesale banking cloud transformations. Journal of Artificial Intelligence General Science (JAIGS) ISSN 3006-4023, 8(1), 291–306. https://doi.org/10.60087/jaigs.v8i1.406

Poudel, A., Sharma, K., Sharma, B. P., Nepal Sanskrit University Beljhundi, Dang, Nepal, Madan Bhandari Memorial College, Department of Computer Science, New Baneshwor, Kathmandu, Nepal, Nepal Engineering College, Department of Software Engineering, Changunarayan, Bhaktapur, Nepal, & Nepal Sanskrit University Beljhundi, Dang, Nepal. (2025). Predictive Modeling for Cloud Migration Costs: A Machine Learning Approach to Estimating Total Cost of Ownership for Enterprises. CLASSICALLIBRARY, 1–14. https://classicalibrary.com/index.php/IJATACSRIA/article/view/2025-FEB-04

Indukuri, N. a. V. (2025). AI-Powered Cloud Migration: Transforming enterprise modernization strategies. Journal of Computer Science and Technology Studies, 7(2), 567–575. https://doi.org/10.32996/jcsts.2025.7.2.60

Soperla, V. B. R. (2025). AUTOMATED DATA MAPPING FOR ENTERPRISE DATA INTEGRATION: LEVERAGING MACHINE LEARNING FOR EFFICIENT DATA TRANSFORMATION. INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING & TECHNOLOGY, 16(1), 3883–3898. https://doi.org/10.34218/ijcet_16_01_268

Upadhyay, D., Manero, J., Zaman, M., & Sampalli, S. (2020). Gradient boosting feature selection with machine learning classifiers for intrusion detection on power grids. IEEE Transactions on Network and Service Management, 18(1), 1104–1116. https://doi.org/10.1109/tnsm.2020.3032618

Xuan, P., Sun, C., Zhang, T., Ye, Y., Shen, T., & Dong, Y. (2019). Gradient Boosting Decision Tree-Based method for predicting interactions between target genes and drugs. Frontiers in Genetics, 10, 459. https://doi.org/10.3389/fgene.2019.00459

Kansara, M., Kaushik, R., & Amazon Web Service (AWS). (2025). Comparative analysis of cloud migration strategies across industries for database migration [Research Article]. International Journal of Current Science (IJCSPUB), 15(2), 507–509. https://www.rjpn.org/ijcspub/papers/IJCSP25B1056.pdf

Loison, F., & Eynard, B. (2023). PLM data transformation: A mesoscopic scale perspective and an industrial case study. Computers in Industry, 155, 104053. https://doi.org/10.1016/j.compind.2023.104053

Downloads

Published

12.11.2025

How to Cite

Rahul Jain. (2025). AI First Framework for Predictive Database Replatforming. International Journal of Intelligent Systems and Applications in Engineering, 13(2s), 61–70. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/7933

Issue

Vol. 13 No. 2s (2025)

Section

Research Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.

IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.