Toward Autonomous Business Intelligence: Research Trends in Automation and Cloud Integration (Published)
Business Intelligence infrastructure is experiencing a fundamental transformation as autonomous systems progressively replace manual intervention paradigms. This evolution extends far beyond basic automation to create self-managing, self-optimizing analytics environments. Cloud integration serves as a critical enabler, allowing for serverless architectures and event-driven responses that continuously adapt to changing conditions. The shift toward autonomy delivers substantial advantages across multiple dimensions: accelerated decision cycles, enhanced analytical accuracy, reduced operational costs, and improved system reliability. Organizations in regulated industries benefit particularly from autonomous governance frameworks that minimize compliance risks while streamlining audit processes. The convergence of artificial intelligence with traditional BI creates environments where predictive maintenance anticipates failures before occurrence, intelligent orchestration dynamically allocates resources based on real-time needs, and policy-as-code models enforce governance automatically. Despite implementation challenges requiring thoughtful approaches to trust-building, legacy integration, human-machine collaboration, and ethical governance, autonomous BI represents a transformative force reshaping how enterprises leverage data assets for competitive advantage.
Keywords: Artificial Intelligence, Cloud integration, autonomous governance, digital twins, quantum-inspired optimization
Quantum-Inspired Optimization of Cloud Infrastructure for Reliability and Cost Efficiency (Published)
Quantum-inspired optimization emerges as a transformative paradigm for cloud infrastructure management, addressing the increasing complexity and multi-dimensional challenges faced by modern distributed systems. This article introduces a comprehensive framework that applies quantum computational principles to classical infrastructure, enabling more efficient navigation of complex solution landscapes without requiring quantum hardware. The framework targets critical operational areas including workload distribution, auto-scaling, resource allocation, and fault tolerance enhancement. By leveraging quantum-inspired algorithmic approaches such as quantum annealing simulations and hybrid methods, the solution demonstrates significant advantages over conventional optimization techniques, particularly as infrastructure complexity increases. The implementation incorporates seamless integration with existing cloud platforms through non-invasive APIs and abstraction layers. Experimental results reveal improved resource utilization, enhanced reliability during failure scenarios, substantial cost reductions, and favorable scalability characteristics. The quantum-inspired approach offers a practical pathway toward addressing the exponentially growing complexity of cloud infrastructure optimization while providing immediate benefits using classical computing resources and establishing foundations for future integration with quantum computing services.
Keywords: Cost Efficiency, Resource Allocation, cloud infrastructure, quantum-inspired optimization, reliability enhancement