Methods for processing data samples in communication networks

A computer-implemented method, performed in a data collecting network node, for acquiring new data samples and for maintaining a set of data samples in a database at a data processing network node, wherein the set of data samples forms input to a function associated with a predictive performance, the method comprising

• obtaining at least one relevance metric, where the relevance metric is indicative of an increase in the predictive performance of the function when using the new data sample as input,
• obtaining a relevance criterion from the data processing network node, where the relevance criterion identifies relevant data samples in a set of data samples based on the at least one relevance metric,
• collecting a set of collected data samples by the data collecting network node,
• selecting one or more relevant data samples from the set of collected data samples based on the relevance metric and on the relevance criterion, where the relevant data samples are associated with relevance metrics meeting the relevance criterion, and
• transmitting the one or more selected data samples to the data processing network node and training a machine learning model arranged to indicate a novelty metric associated with a data sample in a set of collected data samples, wherein the machine learning model constitutes a relevance metric of the at least one relevance metric.