Processing Framework

Processing frameworks are computational systems designed to efficiently manage and analyze diverse data types, enabling advanced tasks like machine learning model training and inference. Current research emphasizes developing unified frameworks capable of handling both structured and unstructured data, including multimodal inputs (e.g., text, images), and addressing challenges like data heterogeneity, scalability, and adversarial attacks. These frameworks are crucial for advancing fields such as natural language processing, network intrusion detection, and clinical diagnostics, improving data quality, and accelerating scientific discovery through efficient data management and analysis.