Inductive Discovery of Industrial Pollution Patterns Machine-Learning Sensor Data Fusion in Heterogeneous Environmental Systems

This article argues that the most informative patterns in industrial environmental monitoring are precisely the ones for which no labels exist an uncharacterized emission source, an emerging nuisance regime, a novel interaction between process state and meteorological condition and that the field therefore requires an inductive programme alongside its supervised one.

We formulate pattern discovery on heterogeneous IoT streams as a five-stage inductive procedure: representation learning from unlabelled sensor responses, cross-stream fusion on a common substrate, candidate pattern emergence, attribution to a physical or process cause, and stabilization under sensor drift. Reviewing the evidence, we show that each stage is individually demonstrated but that the stages have not been composed, and that two obstacles dominate the composition.