  Feature engineering is one of the most important but most tedious tasks in
data science. This work studies automation of feature learning from relational
database. We first prove theoretically that finding the optimal features from
relational data for predictive tasks is NP-hard. We propose an efficient
rule-based approach based on heuristics and a deep neural network to
automatically learn appropriate features from relational data. We benchmark our
approaches in ensembles in past Kaggle competitions. Our new approach wins late
medals and beats the state-of-the-art solutions with significant margins. To
the best of our knowledge, this is the first time an automated data science
system could win medals in Kaggle competitions with complex relational
database.
