Demonstrates improved performance in distributed data management applications, suggesting scalability and reliability.
This paper presents the design and implementation of a Distributed Database System aimed at efficient data storage, processing, and retrieval across multiple nodes. The system uses a client–coordinator– worker architecture, where queries are received by the coordinator and routed to appropriate worker nodes using hash-based partitioning. It supports operations such as data insertion, selection, and distributed query execution. Replication is implemented to ensure data availability, while a fault tolerance mechanism allows the system to continue operation even if a node fails. The coordinator aggregates results from multiple workers, removes duplicates, and returns a consistent output to the client. Experimental results show that the system performs efficiently with improved scalability, reliability, and performance, making it suitable for distributed data management applications.
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Prajwal et al. (2026) studied this question.
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