Context-aware stream data management in edge computing is an emerging research area that focuses on real-time processing, filtering, and analysis of continuous data streams generated by heterogeneous edge devices while considering contextual information such as location, device capabilities, network conditions, and user preferences. Research papers in this domain investigate techniques for adaptive stream processing, dynamic task scheduling, and efficient resource allocation to meet latency-sensitive and QoS-driven requirements for applications such as autonomous vehicles, smart healthcare, industrial IoT, and smart cities. Studies highlight the integration of machine learning, deep learning, and edge intelligence to enable predictive analytics, anomaly detection, and context-aware decision-making directly at the edge, reducing dependence on centralized cloud processing. Recent works also explore security- and privacy-preserving frameworks for stream data, leveraging encryption, federated learning, and blockchain mechanisms to protect sensitive contextual information. Additionally, hierarchical edge–fog–cloud architectures and workload-aware management strategies are being developed to ensure scalability, resilience, and energy efficiency in dynamic and distributed environments. Overall, context-aware stream data management in edge computing is crucial for delivering real-time, intelligent, and adaptive services that respond to both environmental and user-driven contextual factors.