Dataset relevance estimation in storage systems

Abstract

The invention is notably directed to computer-implemented methods and systems for managing datasets in a storage system. In such systems, it is assumed that a (typically small) subset of datasets are labeled with respect to their relevance, so as to be associated with respective relevance values. Essentially, the present methods determine, for each unlabeled dataset of the datasets, a respective probability distribution over a set of relevance values. From this probability distribution, a corresponding relevance value can be obtained. This probability distribution is computed based on distances (or similarities), in terms of metadata values, between said each unlabeled dataset and the labeled datasets. Based on their associated relevance values, datasets can then be efficiently managed in a storage system.