Scalable Computational Frameworks for High-Dimensional and Streaming Data Processing Using Distributed Machine Learning and Edge Computing Architectures
Keywords:
Scalable computing, high-dimensional data, streaming data, distributed machine learning, edge computing, federated learning, parallel processingAbstract
The proliferation of high-dimensional and streaming data has necessitated the development of scalable computational frameworks that leverage distributed machine learning (DML) and edge computing. Traditional centralized systems struggle with real-time processing requirements, creating bottlenecks in data-intensive applications. This paper reviews state of-the-art scalable computational techniques, emphasizing distributed architectures, parallel processing, federated learning, and edge computing. The study discusses various models and methodologies for managing computational efficiency while reducing latency and resource consumption. Additionally, it provides a comparative analysis of different frameworks used for large-scale data processing. Finally, future directions in optimizing high-dimensional and streaming data processing are explored.
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