Abstract: The increasing demand for intelligent, data-driven decision-making in enterprise environments has accelerated the integration of machine learning (ML) models into real-time systems. However, a significant challenge persists in embedding Python-trained ML models into Java-based enterprise applications that require low-latency and high-throughput processing. This study presents a scalable architecture for building Real-Time Decision Support Systems (RT-DSS) that tightly integrates ML inference within Java applications using interoperable model formats like ONNX. A design science methodology was applied to develop and evaluate a five-layer system....
Keywords: Real-Time Decision Support Systems, Machine Learning Integration, Java Enterprise Applications, ONNX Runtime, In-Process Inference, Model Deployment, Apache Kafka, Apache Flink, Spring Boot, Fraud Detection, Low-Latency Systems, MLOps, Model Interoperability
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