Integrating Auto-Transformation Models for Effective Analytics in Context-Aware Fog-Driven Healthcare Systems

This research is concerned with the automated migration from the relational model to NoSQL DB in order to suite the modern big data analytical needs on efficient data management. RDBMS have their drawback when it comes to handling large volumes of unstructured data hence the development of NoSQL and NewSQL. Based on these factors, this study presents an algorithm, Auto JSON, which intelligently performs the JSON conversion from MySQL to MongoDB. The migration select ask unfixed flowing strategies, linking, embedding, and bucketing, while enhancing the swap on source schema features. The algorithm was also tested and run on a cloud MongoDB ATLAS for, with coding done in Python. This can be well explained using experiments implemented in a database of Hospital Management System. The migration process was able to present better time response in performing create and select—better than the original approach. In addition, over the course of the improvements, MongoDB realized an average memory usage decrease of 30%. Such results confirm that the usage of the proposed model will offer a reliable platform for incurring scalable, efficient and reliable big data storage and processing that is essential in current and future sophisticated data-oriented applications.