Key points are not available for this paper at this time.
We investigate the problem of building least squares regression models over training datasets defined by arbitrary join queries on database tables. Our key observation is that joins entail a high degree of redundancy in both computation and data representation, which is not required for the end-to-end solution to learning over joins.
Schleich et al. (Tue,) studied this question.