AI-Assisted Schema Transformation for Automated Legacy-to-Cloud Database Migration
DOI:
https://doi.org/10.63345/sjaibt.v3.i1.301Keywords:
Legacy Database Migration , Cloud Database Migration , Schema Transformation , Artificial Intelligence (AI) , Schema Matching , Automated Data Migration , Machine LearningAbstract
The transition of the legacy database to the cloud environment is one of the key factors for achieving scalability, flexibility, cost efficiency, and data innovation for an organization. One of the most complicated processes in database migration involves the transformation of schemas, meaning their adaptation from the format of the legacy database to a cloud environment. The conventional methods for schema transformation heavily rely on manual work and mapping rules, thus making the process resource-consuming and prone to human errors. The ever-growing complexity of enterprise databases exacerbates these problems, especially in case of heterogeneous systems, unknown schema structure, or constantly changing requirements. Thanks to the recent progress in Artificial Intelligence (AI), there are many opportunities for automating the process of schema transformation within the legacy-to-cloud migration framework. Such methods utilize machine learning, semantic analysis, pattern recognition, knowledge graphs, and language models to identify correspondences between schemas, infer relationships between different data sets, provide suggestions about transformation rules, and create scripts for migration without any human assistance. Such technologies can greatly improve the process of transformation, increase migration accuracy, reduce transformation efforts and facilitate cloud migration. This paper presents an overview of existing literature on AI-assisted schema transformation and automation of the migration process. According to the findings, hybrid approaches, combining traditional matching techniques with advanced AI-assisted semantic analysis, demonstrate greater effectiveness and accuracy of transformation.
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