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This paper presents a migration from relational databases (RDB) to NoSQL databases, leveraging a data model that utilizes concepts such as objects, semantic enrichment, and metadata. It addresses the limitations of previous approaches and extends them to overcome issues encountered in the relational model, allowing for simpler and faster handling of large datasets. This approach eliminates problems related to joins, incorporates the concepts of references and embedded documents, and includes a comparative study between our approach and other methods for migrating from RDB to NoSQL. This comparison covers aspects such as structural changes, insertion rates, selection, and deletion. Automated migration uses document-oriented nested types to store related data in one database record. This simplifies the design phase and reduces queries and updates, speeding up user responses. This research provides a comprehensive summary of the achievement of migrating from a relational database to a document-oriented NoSQL database. The essence of this migration lies in its automatic nature, aimed at transforming the pre-existing relational model into an object-oriented model. This transition aims to align the database structure with MongoDB's documentoriented paradigm, ensuring optimal integration and efficient utilization of the technology's features. This research significantly distinguishes itself from previous work, primarily due to its ability to facilitate automatic database migration. It accomplishes this task by autonomously identifying object concepts, including those related to inheritance. This functionality is unique and represents a significant advancement, as no other migration method has thus far been capable of performing this automatic detection of object concepts, especially inheritance. The result of the migration provides several advantages in terms of reducing record count, particularly as we deal with massive data sets, and in terms of the time required for data deletion and insertion. A prototype shows how effective this automated approach is. Maintain that the migration process is automated. [ABSTRACT FROM AUTHOR]

In this work, we will present an important variant of Halley’s method for solving non-linear equations: Improved Halley’s method (IHM). Analysis of convergence shows that the proposed method is cubically convergent for a simple root. A fairly detailed study of its global convergence will performed. We will prove that the proposed method is very interesting since, under certain conditions, it converges faster than Halley’s method, Super-Halley’s method and Chebyshev’s method which are three of the best iterative techniques. On comparison with the sixth and eighth order methods, they behave either similarly or better for the examples considered. [ABSTRACT FROM AUTHOR]