• Integrates heterogeneous IoT/IoE data streams via a unified Knowledge Graph for semantic interoperability. • Employs Apache Kafka and Flink for real‑time, distributed stream processing pipelines. • Uses SPARQL and SWRL reasoning to discover context‑dependent streams dynamically. • Enables role‑based data access governed by agents’ contextual attributes. • Supports composable, flexible data gathering and transformation pipelines. • Experimental results show improved flexibility, maintainability, and interpretability in Industry 5.0 scenarios.
Article Summaries:
- The paper introduces a context‑aware semantic platform for managing high‑velocity, heterogeneous data streams in Industrial IoT environments. It unifies diverse IoT/IoE data sources through a Knowledge Graph that formally represents devices, streams, agents, transformation pipelines, roles, and rights. The system leverages Apache Kafka and Flink for real‑time processing, while SPARQL queries and SWRL rules enable context‑dependent stream discovery and dynamic, role‑based data access. Experimental results demonstrate that combining semantic modeling, context‑aware reasoning, and distributed stream processing improves interoperability, flexibility, and maintainability of data workflows for Industry 5.0 scenarios.
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