Industrial IoT ecosystems bring together sensors, machines and smart devices operating collaboratively across industrial environments. These systems generate large volumes of heterogeneous, high-velocity data streams that require interoperable, secure and contextually aware management. Most of the current stream management architectures, however, still rely on syntactic integration mechanisms, which result in limited flexibility, maintainability and interpretability in complex Industry 5.0 scenarios. This work proposes a context-aware semantic platform for data stream management that unifies heterogeneous IoT/IoE data sources through a Knowledge Graph enabling formal representation of devices, streams, agents, transformation pipelines, roles and rights. The model supports flexible data gathering, composable stream processing pipelines, and dynamic role-based data access based on agents' contexts, relying on Apache Kafka and Apache Flink for real-time processing, while SPARQL and SWRL-based reasoning provide context-dependent stream discovery. Experimental evaluations demonstrate the effectiveness of combining semantic models, context-aware reasoning and distributed stream processing to enable interoperable data workflows for Industry 5.0 environments.

翻译：工业物联网生态系统集成了传感器、机器和智能设备，使其能够在工业环境中协同运作。这些系统产生大量异构、高速的数据流，需要可互操作、安全且具备情境感知能力的管理机制。然而，当前大多数流管理架构仍依赖于语法层面的集成机制，导致在复杂的工业5.0场景中灵活性、可维护性和可解释性受限。本文提出一种面向数据流管理的情境感知语义平台，该平台通过知识图谱统一异构的物联网/万物互联数据源，实现对设备、数据流、智能体、转换流水线、角色与权限的形式化表示。该模型支持灵活的数据采集、可组合的流处理流水线，以及基于智能体情境的动态角色数据访问；平台依托Apache Kafka与Apache Flink实现实时处理，同时通过基于SPARQL与SWRL的推理提供情境依赖的数据流发现。实验评估表明，结合语义模型、情境感知推理与分布式流处理技术，能够有效实现工业5.0环境下可互操作的数据工作流。