Autonomous Databases: Leveraging Machine Learning and Neural Networks for Predictive Query Optimization, Self-Tuning, and Index Optimization in Multi-RDBMS Systems
DOI:
https://doi.org/10.36676/jrps.v13.i2.1600Keywords:
Autonomous Databases, Machine Learning, Neural NetworksAbstract
Autonomous databases are the new fad in modern database systems. The database systems are managed by machine learning and neural networks for query prediction, self-tuning, and self-indexing. These systems decrease intervention in multi-relational database management systems (multi-RDBMS). This paper analyses the relevance of ML and NN in optimizing the queries and automating the working of databases. Either simulation results of the tested benchmark queries or real-time use cases show the extent of the query processing speed increase and its accuracy. However, problems like implementing these technologies into current structures and dealing with high-velocity data persist. The proposed solutions are using graph neural networks to solve scalability problems. In conclusion, this research enshrines the prospects for AI autonomous databases to improve performance in multi-RDBMS architecture.
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