Design of an Iterative Model for Contextual Structuring of Unstructured NoSQL Data Using Hybrid Topological, Probabilistic, and Relational Learning Models

Abstract

Unstructured exponential growth of NoSQL data is causing an effective challenge for data processing in general, semantic querying, and integration into the traditional analytics pipeline for efficient data processing. Current solutions depend at least in part on rule-based static transformation or partial machine learning models, often lacking any preservation of semantic integrity, schema heterogeneity, or alignment with real-world usage patterns. These constraints severely cripple downstream applications like query optimization, relational mapping, and ML pipeline integration in process. This work proposes a thorough Contextual Structuring Pipeline that automatically converts unstructured NoSQL data into structured, schema-consistent, and optimized relational representations in the queries. The pipeline includes five new learning models designed to contribute to a specific subtask in the transformation. First, CADA-Net (Context-Aware Document Attention Network) uses transformer-based hierarchical encoding to segment raw NoSQL records into semantically meaningful key Value structures. Second, TopoGraph-X constructs an entity-type graph with multi-level topologies from latent document hierarchies through topological learning. Third, HarmoField employs domain-specific BERT embeddings along with the Gaussian mixture model to normalize field representation across heterogeneous sources. Fourth, MetaRel-Frame constructs meta-relational abstractions on logical entity-role patterns to discover relational table blueprints. Finally, QueryStruct transforms the blueprint into a use-optimized dataset through restructuring, indexing, reordering, and compressing its components, taking into account historic query logs. The structural accuracy achieved by this framework is 92.3%, schema normalized is 91.2%, and query latency is decreased by 39.4%. The entire process of structuring unstructured NoSQL data forms a fully automated method, semantically grounded and performance-aware, towards compatibility with SQL-ready, graph analytics, and downstream workflows in machine learning process.