  Many data problems are solved when the right view of a combination of
datasets is identified. Finding such a view is challenging because of the many
tables spread across many databases, data lakes, and cloud storage in modern
organizations. Finding relevant tables, and identifying how to combine them is
a difficult and time-consuming process that hampers users' productivity.
  In this paper, we describe Dataset-On-Demand (DoD), a system that lets users
specify the schema of the view they want, and have the system find views for
them. With many underlying data sources, the number of resulting views for any
given query is high, and the burden of choosing the right one is onerous to
users. DoD uses a new method, 4C, to reduce the size of the view choice space
for users. 4C classifies views into 4 classes: compatible views are exactly the
same, contained views present a subsumption relationship, complementary views
are unionable, and contradictory views have incompatible values that indicate
fundamental differences between views. These 4 classes permit different
presentation strategies to reduce the total number of views a user must
consider.
  We evaluate DoD on two key metrics of interest: its ability to reduce the
size of the choice space, and the end to end performance. DoD finds all views
within minutes, and reduces the number of views presented to users by 2-10x.
