AI-Assisted Dynamic Modeling for Data Management in a Distributed System

Data management is an administrative mechanism that involves the acquisitions, validations, storage, protection, and processing of data needed by its users to ensure that data are accessible, reliable, and timely. It is a challenging task to manage protections for information properties. With the emphasis on distributed systems and Internet-accessible systems, the need for efficient information security management is increasingly important. In the paper, artificial intelligence-assisted dynamic modeling (AI-DM) is used for data management in a distributed system. Distributed processing is an effective way to enhance the efficiency of database systems. Therefore, each distributed database structure’s functionality depends significantly on its proper architecture in implementing fragmentation, allocation, and replication processes. The proposed model is a dynamically distributed internet database architecture. This suggested model enables complex decision-making on fragmentation, distribution, and duplication. It provides users with links from anywhere to the distributed database. AI-DM has an improved allocation and replication strategy where no query performance information is accessible at the initial stage of the distributed database design. AI-DM findings show that the proposed database model leads to the reliability and efficiency of the enhanced system. The final results are obtained by analyzing the dynamic modeling ratio is 87.6%, increasing decision support ratio is 88.7%, the logistic regression ratio is 84.5%, the data reliability ratio is 82.2%, and the system ratio is 93.8%.

Fragmentation in Distributed Database Design Based on Ant Colony Optimization Technique

Distributed database design solutions depend heavily on the exploitation of input data sources by using clustering techniques in data mining. A new approach of biomimetic computation systems such as ant colony optimization (ACO) for this solution is of interest to informatics experts. Using ACO techniques for this solution has the advantages such as faster algorithms thanks to the randomness of ant colony behavior. The use of random numbers based on heuristic information to pickup (drop) points will facilitate the flexible search on a large data space, so that it provides us with a better answer. In this article, the authors present ACO algorithms application solutions to clustering techniques for the problem of vertical fragmentation of distributed data.

Key agreement based secure Kerberos authentication protocol (KASKAP) for distributed database access in secured manner

Distributed database is a collection of multiple databases that can be stored at different network sites. It acts as an important role in today’s world intended for storing and retrieving huge data. The implementation of distributed database advantages such as data replication, low operating costs, faster data transaction and data processing, but security is still a significant problem. In this paper make clear to explain security issues of distributed database and give the suggestion to improve security of distributed database. Subsequently, secured distributed database design in light of trusted node is proposed. The design contains a unique node in a system called a trusted node for each site through which every single other node will get to the database. Trusted node process client demands, joins the outcomes from concerned distributed databases and forward it to the confirmed client. The system adjusted by the trusted nodes keeping in mind the end goal to give authentication is Key Agreement based Secure Kerberos Authentication Protocol (KASKAP). Hence authenticated users can only access the database.