Journal Article

The Future of Mainframe IDMS: Leveraging Artificial Intelligence for Modernization and Efficiency

Govindaraj, Vasanthi

2024

Overview

IDMS (Integrated Database Management System) has long been a backbone for mission-critical systems in finance, healthcare, and government sectors. However, the rigid architecture of legacy systems poses challenges in scalability, flexibility, and integration with modern technologies. This paper explores IDMS modernization using Artificial Intelligence (AI), with a focus on predictive maintenance, query optimization, and cloud integration. Through real-world implementations, the integration of AI-driven solutions has shown transformative potential: query response times were reduced by 25%, unscheduled downtime decreased by 30%, and system scalability improved by accommodating a 40% increase in traffic without degradation. By leveraging AI-powered automation and modern cloud infrastructures, IDMS can achieve database optimization and real-time operational efficiency. This work highlights how AI ensures the relevance and competitiveness of IDMS, enabling it to meet the demands of modern legacy systems and ensuring its sustained role in critical business operations.

• Publisher: Science and Information (SAI) Organization Limited
• Subject: Artificial intelligence / Automation / Computer science / Data base management systems / Data integrity / Downtime / Efficiency / Flexibility / Legacy systems / Machine learning / Mainframes / Modernization / Optimization / Optimization algorithms / Optimization techniques / Predictive analytics / Predictive maintenance / Queries / Real time operation / Relational data bases / Response time (computers)