Intelligent Enterprise Integration Using Event-Driven Microservices and Predictive Analytics for Scalable Business Operations
DOI:
https://doi.org/10.15680/IJCTECE.2023.0605020Keywords:
Event-Driven Architecture, Microservices, Enterprise Integration, Predictive Analytics, Cloud-Native Computing, Real-Time Data Processing, Business Intelligence, Scalability, Machine Learning, Operational OptimizationAbstract
Intelligent enterprise integration has become essential for organizations seeking scalable, responsive, and data-driven business operations in increasingly complex digital environments. Traditional enterprise integration architectures often encounter challenges related to system interoperability, processing latency, data inconsistency, and limited predictive capabilities. This research proposes an intelligent enterprise integration framework that combines event-driven microservices, predictive analytics, and scalable cloud-native infrastructure to improve operational efficiency and business responsiveness. The framework enables independent enterprise services to communicate through asynchronous events, supporting real-time information exchange across business applications, customer relationship management, enterprise resource planning, and supply chain management systems. Predictive analytics models process historical and streaming operational data to identify emerging patterns, forecast demand fluctuations, anticipate resource requirements, and support proactive decision-making. A modular architecture incorporates event brokers, API gateways, data processing pipelines, machine learning components, and centralized monitoring mechanisms. The proposed methodology evaluates integration performance through response latency, throughput, service availability, prediction accuracy, resource utilization, and operational scalability. Comparative experiments against conventional synchronous integration architectures are designed to assess the effectiveness of the framework under varying workloads and simulated operational conditions. The expected outcomes include improved interoperability, reduced processing delays, enhanced forecasting capabilities, and increased resilience during workload fluctuations. This approach provides a practical foundation for intelligent enterprise modernization, enabling organizations to coordinate distributed applications, optimize resource allocation, and establish adaptive business operations while maintaining security, reliability, and long-term architectural flexibility.References
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