Abstract

Real-time stream reasoning has emerged as a pivotal methodology in processing and extracting actionable knowledge from continuous, high-velocity data streams generated by sensors, social media, financial systems, and IoT devices. Unlike traditional batch analytics, stream reasoning enables systems to infer, update, and adapt knowledge in near real time, delivering timely insights for decision support, anomaly detection, and automated response. This paper investigates the foundational techniques, architectures, and algorithms for real-time stream reasoning, emphasizing continuous knowledge extraction from dynamic data sources. We explore rule-based systems, windowing models, semantic reasoning with ontologies, and hybrid learning–logic integration. A comprehensive literature review highlights evolving frameworks like C-SPARQL, ETALIS, and CQELS that address challenges of low latency, high throughput, and semantic expressivity. Our research methodology involves qualitative analysis and comparative evaluation of stream reasoning pipelines under varying workload conditions. Key advantages include immediate decision support, scalable processing, and improved contextual understanding. However, limitations such as computational overhead, memory constraints, and complexity of semantic models remain. Results indicate that hybrid approaches combining statistical learning with rule-based inference deliver superior performance in dynamic environments. We conclude with potential avenues for optimizing reasoning strategies and future integration with edge computing and distributed knowledge graphs.