Authors : Karan Lande; Priyanka Bhore

Volume/Issue : Volume 9 - 2024, Issue 11 - November

Google Scholar : https://tinyurl.com/4vbc79xd

Scribd : https://tinyurl.com/3kjwye92

DOI : https://doi.org/10.38124/ijisrt/IJISRT24NOV1373

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : This paper explores the integration of artificial intelligence (AI) in extensive database management systems (DBMS) to enhance data processing, retrieval, and decisionmaking. As data volumes continue to grow exponentially, traditional database management techniques struggle to maintain efficiency and accuracy. We present a framework that leverages AI technologies, Such as machine learning and natural language processing-to automatically classify data, optimize query performance, and enhance data integrity. Our method allows for adaptive learning from user interactions and patterns by implementing AI-driven algorithms. allowing for realtime adjustments and predictive analytics. Case studies demonstrate significant improvements in data accessibility, user experience, and operational efficiency. The findings suggest that AI-enhanced DBMS not only streamline data management tasks but also empower organizations to derive deeper insights from their data assets, ultimately driving informed decision-making and fostering innovation. Future research directions are proposed to further explore the scalability and security implications of AI in database management.

Keywords : Artificial Intelligence (AI), Database Management Systems (DBMS), Machine Learning, Deep Learning, Natural Language Processing (NLP), Realtime Data Processing, Data Security, Adaptive Learning, Database Optimization, Automation in Database Management.

References :

1. Artificial Intelligence for Database Management: Techniques and Applications"by S. M. Thamp This book explores various AI techniques that can enhance database management systems, including case studies and applications in different domains.
2. "Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data" by Daniel G. P. G. de Almeida and others It provides insights into how AI and machine learning can be integrated into big data analytics, which is crucial for managing extensive databases.
3. "A Survey on Artificial Intelligence for Data Management in Big Data" by Mohamed S. Kamel and others This paper surveys the integration of AI techniques in big data management, discussing various methodologies and their effectiveness.
4. "AI in Data Management: Opportunities and Challenges"by R. H. Kuo, et al. This article discusses the potential applications of AI in data management, including the challenges and future directions of research.
5. IBM's AI-Powered Database Management: IBM offers resources detailing how AI can improve database management through automation, predictive analytics, and machine learning. You can explore their insights on AI in database management [here] (https://www.ibm.com/cloud/learn/ai-in-databasemanagement).
6. Microsoft Azure AI for Databases Microsoft Azure provides a suite of tools that leverage AI for database management. They provide various use cases and documentation on implementing AI within Azure databases, which can be found [here](https://azure.microsoft.com/enus/services/cognitive-services/).
7. Google Cloud AI and BigQuery:: Google Cloud offers resources on using AI with BigQuery, their serverless data warehouse that allows for extensive data management and analysis. More information can be found [here](https://cloud.google.com/bigquery).
8. ACM SIGMOD Conference: This annual conference focuses on database management, where many papers on the integration of AI in databases are presented.
9. IEEE Transactions on Knowledge and Data Engineering:  A journal that frequently publishes research on advanced database management systems, including AI applications.