Graph-Based Data Engineering Models for Large-Scale Knowledge Discovery

  • Authors

    • H. N. Mahabala Computer Scientist, Tata Institute of Fundamental Research, India. Author

    DOI:

    https://doi.org/10.67228/30715717/IJDEIC-2025PII3W9L

    Published 08-03-2025

  • Graph Data Engineering, Knowledge Graph, Graph Database, Graph Neural Networks, Large-Scale Analytics, Knowledge Discovery, Semantic Networks, Artificial Intelligence, Graph Mining, Data Integration

    Issue

    Vol. 8 No. 2 (2025)

    Section

    Articles

    How to Cite

    [1]
    M. H. N, “Graph-Based Data Engineering Models for Large-Scale Knowledge Discovery”, IJDEIC, vol. 8, no. 2, pp. 01–16, Aug. 2025, doi: 10.67228/30715717/IJDEIC-2025PII3W9L.

  • Abstract

    Graph-based data engineering has become a powerful approach for managing and analyzing highly interconnected data across enterprise systems, IoT, social media, healthcare, finance, and scientific domains. Unlike traditional relational databases, graph-based models represent data as interconnected nodes and edges, enabling efficient relationship analysis, semantic understanding, and knowledge discovery. This paper surveys recent advances in graph databases, knowledge graphs, graph neural networks (GNNs), and distributed graph analytics, and proposes an integrated framework for scalable graph construction, semantic enrichment, graph analytics, and AI-driven knowledge extraction. The framework emphasizes scalability, semantic consistency, explainable AI, and continuous graph evolution. Experimental evaluation demonstrates improved relationship discovery, query performance, and knowledge extraction compared with conventional relational approaches, making the proposed framework suitable for intelligent applications in healthcare, cybersecurity, finance, smart manufacturing, and enterprise knowledge management.

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