  Data scientists often draw on multiple relational data sources for analysis.
A standard assumption in learning and approximate query answering is that the
data is a uniform and independent sample of the underlying distribution. To
avoid the cost of join and union, given a set of joins, we study the problem of
obtaining a random sample from the union of joins without performing the full
join and union. We present a general framework for random sampling over the set
union of chain, acyclic, and cyclic joins, with sample uniformity and
independence guarantees. We study the novel problem of the union of joins size
evaluation and propose two approximation methods based on histograms of columns
and random walks on data. We propose an online union sampling framework that
initializes with cheap-to-calculate parameter approximations and refines them
on the fly during sampling. We evaluate our framework on workloads from the
TPC-H benchmark and explore the trade-off of the accuracy of union
approximation and sampling efficiency.
