Machine learning typically requires training and validation of models with large and heterogeneous datasets. The engineering of these datasets is a critical task for enabling high accuracy and generalization, although in many cases it is done following an ad-hoc approach. Hyperknowledge can enable more structured engineering of datasets, by representing the datasets' symbolic and non-symbolic information, within the same framework, and enabling queries for dataset creation, retrieval, resampling, and combination. In this poster, we present how the Hyperknowledge Platform evaluates those queries and analyze its performance quantitatively. The preliminary results indicate that our platform can support data scientists' work while adding negligible time overhead.