AML-BDA: A Sector-Aware Adaptive Multi-Layer Big Data Architecture for Scalable and Intelligent Cross-Industry Analytics

Abstract

The rapid proliferation of high-volume, high-velocity, and high-variety data across industries has intensified the need for scalable and adaptive Big Data architectures. Traditional distributed frameworks often suffer from static pipeline configurations, limited domain awareness, and inefficient resource utilization, thereby constraining cross-industry analytics performance. This research proposes an Adaptive Multi-Layer Big Data Architecture (AML-BDA) designed to address these limitations through sector-aware abstraction and dynamic orchestration. The proposed framework integrates ontology-driven domain mapping, workload-based auto-scaling, intelligent model selection, and continuous feedback optimization within a unified architecture. AML-BDA introduces a Domain Abstraction Layer to harmonize heterogeneous datasets and an Adaptive Processing Layer that dynamically reconfigures computational resources based on real-time workload metrics. Experimental validation across healthcare, financial fraud detection, smart manufacturing, and retail forecasting datasets demonstrates significant improvements in processing latency, throughput, scalability, and predictive accuracy compared to conventional architectures. Results show latency reduction of up to 35%, throughput enhancement exceeding 20%, and prediction accuracy gains of approximately 5–8%. The findings confirm that integrating sector-aware metadata intelligence with adaptive orchestration mechanisms enhances both operational efficiency and analytical performance. This study contributes a scalable, interoperable, and intelligent Big Data framework capable of supporting next-generation data-driven decision systems across heterogeneous industrial environments.