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Abstract

The rapid deployment of NoSQL databases to manage large complex data, unstructured, and sample dataset has posed huge challenges for the efficient processing and analysis of such data samples. Current approaches, particularly those using rule-based schemes for schema detection and query optimization, fail to address the dynamic and heterogeneous nature of NoSQL data samples. To address these shortcomings, this work proposes an overall framework of integrating several advanced methods based on machine learning into NoSQL data processing and analysis to improve efficiency. This work begins with Relational Graph Convolutional Network, a method that dynamically infers schema from the NoSQL database. This helps in automatically detecting intricate relationships within data, reducing schema processing timestamp by 30%. We extend TPOT with transformers-BERT and convolutional neural networks-ResNet for feature selection of multimodal data text, image, and tabular, improving accuracy by 15-20% against the model. MMT allows us to fuse disparate types of data into a shared latent space, lifting multimodal classification accuracy from 78% to 90%. We use DQL-based optimization learning from past query performance to reduce the average query execution timestamp by 33%. Finally, we employ Hierarchical Attention Networks for analyzing nested NoSQL structures; this improved the classification performance with a boost in the F1-score from 0.78 to 0.88. This combined approach mainly results in improved schema inference, feature selection, multimodal data fusion, and query optimization, and it leads to significant performance gains for NoSQL-based systems that pave the way for efficient treatment of large-scale, heterogeneous datasets & samples.