  A coreset is a small set that can approximately preserve the structure of the
original input data set. Therefore we can run our algorithm on a coreset so as
to reduce the total computational complexity. Conventional coreset techniques
assume that the input data set is available to process explicitly. However,
this assumption may not hold in real-world scenarios. In this paper, we
consider the problem of coresets construction over relational data. Namely, the
data is decoupled into several relational tables, and it could be very
expensive to directly materialize the data matrix by joining the tables. We
propose a novel approach called ``aggregation tree with pseudo-cube'' that can
build a coreset from bottom to up. Moreover, our approach can neatly circumvent
several troublesome issues of relational learning problems [Khamis et al., PODS
2019]. Under some mild assumptions, we show that our coreset approach can be
applied for the machine learning tasks, such as clustering, logistic regression
and SVM.
