Method and system for producing a semantic mapping of sensor data

Abstract

A classification model is trained with elements from several data sources, with the elements including sensor data mounted in an industrial plant, and with the labels indicating a semantic type for each of the elements. The classification model is retrained with an adaptive learning algorithm implementing active learning and/or incremental learning, until the classification model is capable of mapping each element of the data sources to one of the semantic types. The method and system provide a semantic mapping for sensor data. The automated or semi-automated creation of the semantic mapping loosens the coupling between a domain expert and data scientist, serves as a bridge and reduces workload, speeding up data modeling and data integration steps. It provides inexperienced users with access to domain expertise. Re-use of data models is facilitated, which simplifies further integration and exchange activities. The adaptive learning algorithm provides an incremental enhancement of the classification model.