AI-Augmented Gray Relational Modeling for Multi-Tenant SAP HANA Clouds: Petabyte-Scale Apache Processing for Fraud Detection, Adaptive Risk Intelligence, and Cybersecurity
DOI:
https://doi.org/10.15662/IJEETR.2024.0606010Keywords:
AI-augmented analytics, Gray Relational Modeling (GRM), multi-tenant SAP HANA Cloud, petabyte-scale processing, Apache data pipelines, fraud detection, adaptive risk intelligence, cybersecurity, anomaly detection, distributed in-memory computingAbstract
The rapid expansion of multi-tenant SAP HANA Cloud environments has intensified the need for scalable, intelligent, and secure analytics frameworks. This paper introduces an AI-augmented Gray Relational Modeling (AI-GRM) approach designed to analyze complex, high-dimensional behavioral data across petabyte-scale Apache processing pipelines. The proposed architecture integrates deep neural embeddings with classical GRM to enhance pattern discrimination, anomaly sensitivity, and cross-tenant relational weighting. Leveraging real-time stream ingestion and distributed in-memory computing, the system supports high-throughput fraud detection, adaptive risk intelligence, and continuous cybersecurity monitoring. Experimental evaluations with synthetic and enterprise-scale logs indicate that AI-GRM improves detection precision and relational clarity under sparse, noisy, and multidomain data conditions. The framework demonstrates strong adaptability for regulatory compliance, cyber-threat forensics, and enterprise-grade operational resilience. Findings highlight the potential of hybrid gray-system intelligence and machine learning to advance next-generation cloud security strategies.References
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