Research on Context-aware Stream Data Management in Edge Computing focuses on designing intelligent frameworks and algorithms to collect, process, and manage continuous data streams from IoT and edge devices while considering contextual information such as location, time, device status, and user behavior. This area addresses challenges including high-velocity data, heterogeneous and resource-constrained edge devices, dynamic workloads, and the need for real-time decision-making. Key research directions include context-aware data filtering, aggregation, and preprocessing techniques, adaptive stream processing frameworks, and machine learning–based analytics for anomaly detection, prediction, and resource optimization. Other emerging topics involve edge–cloud collaborative stream management, energy- and latency-aware processing, privacy-preserving context modeling, and multi-objective optimization for throughput, accuracy, and efficiency. Additionally, research on fault-tolerant and scalable context-aware stream data pipelines, event-driven processing, and intelligent resource allocation represents significant avenues for advancing real-time, efficient, and intelligent edge computing systems.