AI-Enabled Secure Enterprise Data Platforms for Predictive Analytics Fraud Detection Compliance Automation and Scalable Cloud Transformation
DOI:
https://doi.org/10.15662/IJEETR.2025.0703001Keywords:
Artificial Intelligence (AI), Predictive analytics, Fraud Detection, Compliance Automation, Cloud Transformation, Enterprise Data Platforms, Machine Learning, Cybersecurity, Data Governance, Distributed Computing, Cloud-Native Architecture, Real-Time Analytics, DevSecOps, Zero Trust Security, Data Engineering, Scalable Systems, Intelligent Automation, Enterprise Cloud, Risk Prediction, Digital TransformationAbstract
Graph-based data modeling has emerged as a powerful approach for analyzing complex relationships in diverse domains such as social networks, bioinformatics, recommendation systems, and knowledge graphs. Unlike traditional relational data models, graph models inherently capture entities and their intricate interconnections through nodes and edges, enabling more natural and effective analysis of relational data. The increasing volume and complexity of data generated in modern applications demand scalable, expressive, and flexible modeling techniques capable of uncovering hidden patterns and insights. Recent advances in graph databases, graph neural networks (GNNs), and graph analytics algorithms have significantly enhanced the ability to model and interpret complex relationships. These developments facilitate tasks such as community detection, link prediction, anomaly detection, and influence propagation with higher accuracy and computational efficiency. This paper presents a comprehensive review of graph-based data modeling techniques, emphasizing their role in complex relationship analysis. We explore state-of-the-art methodologies including heterogeneous graph modeling, dynamic graph analysis, and multi-relational graph representations. Moreover, we examine the integration of graph data models with machine learning frameworks to boost predictive analytics capabilities. The challenges of large-scale graph processing, data sparsity, and evolving graph structures are discussed, alongside emerging solutions such as distributed graph processing platforms and attention-based GNNs. Through case studies in social media analytics, fraud detection, and biomedical networks, the practical impact of graph-based modeling is illustrated. We conclude by highlighting future research directions including explainability in graph models, privacy-preserving graph analytics, and real-time graph processing. This study underscores the transformative potential of graph-based data modeling in unraveling complex relationships, driving innovations in data science and analytics in 2024 and beyond.
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