The Role of Machine Learning in Predicting Data Versioning Needs for Historical Repositories

Abstract

In the era of big data, managing and preserving historical datasets is crucial for various applications, including scientific research, business intelligence, and legal compliance. Data versioning, the process of managing and storing multiple versions of datasets, plays a critical role in ensuring data integrity, reproducibility, and traceability. This paper explores the application of machine learning techniques in predicting data versioning needs for historical repositories. By analyzing historical data usage patterns, we aim to develop models that can forecast versioning requirements, thereby optimizing storage and retrieval processes. Our findings demonstrate that machine learning can significantly enhance the efficiency of data versioning strategies, ultimately contributing to better data management practices.