ML-driven automation optimizes routine tasks like backup and recovery, capacity planning and database provisioning

Paper ID: EIJTEM_2023_10_1_22-31

Author's Name: Padmaja Pulivarthy

Volume: 10

Issue: 1

Year: 2023

Page No: 22-31

Abstract:

Recently, it has become evident that a distributed workforce is the future of work, and that future is already here. Therefore, it is crucial to understand that AI-DB integration is vital not only for the practical application of artificial intelligence technology and the advancement of database technology but also for the next generation of computing. This integration will support future Intelligent Information Systems, enhancing work efficiency and productivity. AI-DB integration will significantly contribute to the infrastructure of science and technology, businesses, and humanitarian applications of computers. Given its potential contributions, AI-DB integration is far more important than its impact on AI and database technologies alone might suggest. This review discusses various concepts, emphasizing key areas such as designing Intelligent Database Interfaces (IDIs), learnable databases, and Smart Query. These fundamental areas guided our investigation into how AI enhances database efficiency by optimizing query performance, automating routine management tasks, and strengthening data security. Additionally, the paper outlines short-term and long-term application areas where AI and databases converge, providing a comprehensive overview of the progress, challenges, and opportunities. The review reflects the opinions of various authors and experts on the necessity and importance of AI-DB integration for the future generation of computing.

Keywords: ML-driven automation, capacity planning, database provisioning

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