Optimizing Transactional Integrity with AI in Distributed Database Systems

Abstract

In the era of big data and distributed systems, ensuring transactional integrity has become a pivotal concern for organizations. Traditional methods of maintaining consistency and reliability in distributed databases often fall short in meeting the demands of high-volume transactions and dynamic data environments. This paper presents a novel approach that leverages Artificial Intelligence (AI) to optimize transactional integrity in distributed database systems. By integrating AI techniques such as machine learning algorithms and anomaly detection, our framework enhances the traditional consensus protocols and improves conflict resolution processes. We conduct extensive experiments to evaluate the performance of the proposed solution against established benchmarks, revealing significant improvements in transaction throughput, latency, and integrity maintenance. The results indicate that AI-driven approaches can not only enhance the efficiency of transactional operations but also bolster the reliability and consistency of data across distributed environments. This research contributes to the growing field of intelligent data management, offering a compelling case for the adoption of AI technologies in achieving superior transactional integrity in distributed database systems.