  Many data we collect today are in tabular form, with rows as records and
columns as attributes associated with each record. Understanding the structural
relationship in tabular data can greatly facilitate the data science process.
Traditionally, much of this relational information is stored in table schema
and maintained by its creators, usually domain experts. In this paper, we
develop automated methods to uncover deep relationships in a single data table
without expert or domain knowledge. Our method can decompose a data table into
layers of smaller tables, revealing its deep structure. The key to our approach
is a computationally lightweight forward addition algorithm that we developed
to recursively extract the functional dependencies between table columns that
are scalable to tables with many columns. With our solution, data scientists
will be provided with automatically generated, data-driven insights when
exploring new data sets.
