Enhancing Data Integration in Oracle Databases: Leveraging Machine Learning for Automated Data Cleansing, Transformation, and Enrichment
Abstract
The integration of data from multiple sources into Oracle databases presents significant challenges, including data cleansing, transformation, and enrichment. Traditional methods often involve manual processes that are time-consuming, error-prone, and inefficient. This research explores the application of machine learning (ML) algorithms to automate and enhance these processes, thereby improving the overall efficiency and accuracy of data integration. In this study, we develop and evaluate a comprehensive ML-based framework designed to address the complexities of data integration. The framework leverages supervised and unsupervised learning techniques to identify and correct inconsistencies, transform data into compatible formats, and enrich datasets with additional relevant information. Key components of the framework include data preprocessing modules, anomaly detection algorithms, and intelligent transformation pipelines. We conducted extensive experiments using diverse datasets sourced from different domains to assess the performance of the proposed framework. The results demonstrate significant improvements in data quality and integration speed compared to traditional methods. The automated processes reduced the time required for data preparation by up to 70% and increased the accuracy of integrated data by 25%. Furthermore, this research highlights the adaptability of ML algorithms in handling various data types and formats, showcasing their potential in real-world applications. The implementation details, including algorithm selection, model training, and system architecture, are thoroughly discussed to provide a clear roadmap for practitioners and researchers interested in replicating or extending this work. This paper contributes to the field of data integration by presenting a novel approach that combines the strengths of ML algorithms with the robustness of Oracle databases. The findings underscore the transformative impact of ML in automating and optimizing data integration tasks, paving the way for more efficient and reliable data management solutions in complex, multi-source environments.
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