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Industry 4.0 offers opportunities to combine multiple sensor data sourcesusing IoT technologies for better utilization of raw material in productionlines. A common belief that data is readily available (the big dataphenomenon), is oftentimes challenged by the need to effectively acquirequality data under severe constraints. In this paper we propose a designmethodology, using active learning to enhance learning capabilities, forbuilding a model of production outcome using a constrained amount of rawmaterial training data. The proposed methodology extends existing activelearning methods to effectively solve regression-based learning problems andmay serve settings where data acquisition requires excessive resources in thephysical world. We further suggest a set of qualitative measures to analyzelearners performance. The proposed methodology is demonstrated using an actualapplication in the milk industry, where milk is gathered from multiple smallmilk farms and brought to a dairy production plant to be processed into cottagecheese.

From Limited Annotated Raw Material Data to Quality Production Data: A Case Study in the Milk Industry (Technical Report)