A Research Paper on: AI/ML-Based Data Duplication Alert System

Data duplication is a pervasive issue across organizations dealing with extensive data, leading to wasted storage, increased processing costs, and compromised data integrity. Traditional methods for identifying and managing data duplication are often time-consuming and inefficient, especially as data volumes continue to scale. To address these challenges, we propose an AI/ML-Based Data Duplication Alert System, leveraging machine learning algorithms to intelligently detect and alert users to potential data duplication. The system employs advanced techniques such as natural language processing (NLP), pattern recognition, and clustering to analyze data structures and content across databases, documents, and storage locations. By utilizing both supervised and unsupervised learning models, it can detect duplicate data entries even when they include typos or structural variations. Models are evaluated using statistical metrics such as Receiver Operating Characteristic (ROC) curves, precision, recall, and accuracy rates exceeding 95%, ensuring high reliability in detecting duplicates. In addition to real-time alerts, the system integrates seamlessly with data management workflows, preventing duplicate entries at the point of data entry, thus upholding data quality standards. This AI/ML-based solution automates the detection process, enabling faster response times, reducing storage requirements, and improving data accuracy. By ensuring data consistency, the system promotes more efficient data utilization across organizational systems while maintaining a high standard of accuracy and precision.