  This work is motivated by two key facts. First, it is highly desirable to be
able to learn and perform knowledge discovery and analytics (LKD) tasks without
the need to access raw-data tables. This may be due to organizations finding it
increasingly frustrating and costly to manage and maintain ever-growing tables,
or for privacy reasons. Hence, compact models can be developed from the raw
data and used instead of the tables. Second, oftentimes, LKD tasks are to be
performed on a (potentially very large) table which is itself the result of
joining separate (potentially very large) relational tables. But how can one do
this, when the individual to-be-joined tables are absent? Here, we pose the
following fundamental questions: Q1: How can one "join models" of
(absent/deleted) tables or "join models with other tables" in a way that
enables LKD as if it were performed on the join of the actual raw tables? Q2:
What are appropriate models to use per table? Q3: As the model join would be an
approximation of the actual data join, how can one evaluate the quality of the
model join result? This work puts forth a framework, Model Join, addressing
these challenges. The framework integrates and joins the per-table models of
the absent tables and generates a uniform and independent sample that is a
high-quality approximation of a uniform and independent sample of the actual
raw-data join. The approximation stems from the models, but not from the Model
Join framework. The sample obtained by the Model Join can be used to perform
LKD downstream tasks, such as approximate query processing, classification,
clustering, regression, association rule mining, visualization, and so on. To
our knowledge, this is the first work with this agenda and solutions. Detailed
experiments with TPC-DS data and synthetic data showcase Model Join's
usefulness.
