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ProbSPARQL is an upward-compatible SPARQL extension that models uncertain numeric values as random variables with probabilistic RDF literal datatypes, enabling distribution-aware queries, probabilistic filters, and divergence-based joins. Implemented on Apache Jena ARQ, it addresses challenges in querying multi-dimensional uncertain measurement data from circular manufacturing knowledge graphs.
This paper proposes an Edge-AI-driven decentralized task allocation framework for circular smart manufacturing that uses learning-to-rank to align with the ordering-based nature of winner selection. Simulation results show improved delay, deadline adherence, and energy efficiency under high-load and tight-deadline scenarios.