The role of cloud computing, artificial intelligence, and blockchain in Big Data processing

In the era of digital technologies, working with large volumes of data is undergoing significant changes due to new technologies. This article presents a theoretical and methodological description and analysis of the integration of technologies such as artificial intelligence, machine learning, cloud computing, and blockchain to address complex tasks in data processing and information management. The subject of the study is the development of an efficient data storage system to solve applied tasks in various scientific fields. Cloud computing provides seamless access to digital resources, supports remote services, and reduces dependence on physical infrastructure by hosting databases and management systems on virtual platforms. This fosters scalability and distribution of computational power for processing vast amounts of information and deploying complex artificial intelligence models. Artificial intelligence, particularly automated machine learning (AutoML) and generative models, automates labor-intensive stages of data preparation, significantly enhancing the efficiency and accuracy of models. Blockchain technology, in turn, addresses critical issues of security, data integrity, and digital rights management, creating a trusted and immutable environment for decentralized data processing systems. The joint application of these technologies lays the foundation for the creation of self-optimizing, scalable, and secure automated data processing systems, as demonstrated by examples in library science, healthcare, and linguistics. For instance, blockchain technology ensures the transparency, reliability, and security of library records. It plays an important role in managing intellectual property, digital rights management (DRM), and authenticating rare or archival documents. In linguistics, we note the ability of data structures to handle linguistic ambiguity and demonstrate complex relationships between language elements, using logical input mechanisms and principles of synergetics. Further work is expected to develop more advanced automation technologies for rapid deployment of trained models with fewer data and the potential for use in various fields.