Data Oriented Architecture

Data-oriented architecture (DOA) prioritizes data accessibility and reusability to improve efficiency and scalability in data-driven systems. Current research focuses on applying DOA principles to diverse areas, including machine learning deployment, network slicing, and large-scale analytics, often leveraging machine learning algorithms for tasks like semantic equivalence detection and resource orchestration. This approach aims to address challenges in managing increasingly complex and heterogeneous data, ultimately leading to more robust, adaptable, and efficient systems across various domains. The impact is seen in improved resource utilization, faster processing times, and enhanced data integration capabilities.