Deep Learning for Automated Data Profiling and Pattern Recognition in Large-Scale Datasets
Abstract
The rapid expansion of large-scale datasets across modern digital ecosystems has created an urgent need for automated, accurate, and scalable data understanding mechanisms. This paper presents an advanced deep learning–driven framework for automated data profiling and pattern recognition, designed to address challenges in data quality assessment, anomaly detection, and structural insight generation. The proposed approach leverages neural architectures such as autoencoders, convolutional networks, and transformer-based models to learn complex feature relationships and detect latent patterns with minimal manual intervention. By integrating statistical profiling with representation learning, the framework enhances the discovery of hidden correlations, semantic structures, and irregularities within heterogeneous datasets. Experimental evaluations on multiple real-world and synthetic datasets demonstrate significant improvements in profiling accuracy, anomaly recognition, and interpretability compared to traditional rule-based and machine learning–based methods. The findings highlight the potential of deep learning to revolutionize data governance, analytics pipelines, and large-scale information management by enabling continuous, automated, and intelligent data understanding
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