  Database analytics algorithms leverage quantifiable structural properties of
the data to predict interesting concepts and relationships. The same
information, however, can be represented using many different structures and
the structural properties observed over particular representations do not
necessarily hold for alternative structures. Thus, there is no guarantee that
current database analytics algorithms will still provide the correct insights,
no matter what structures are chosen to organize the database. Because these
algorithms tend to be highly effective over some choices of structure, such as
that of the databases used to validate them, but not so effective with others,
database analytics has largely remained the province of experts who can find
the desired forms for these algorithms. We argue that in order to make database
analytics usable, we should use or develop algorithms that are effective over a
wide range of choices of structural organizations. We introduce the notion of
representation independence, study its fundamental properties for a wide range
of data analytics algorithms, and empirically analyze the amount of
representation independence of some popular database analytics algorithms. Our
results indicate that most algorithms are not generally representation
independent and find the characteristics of more representation independent
heuristics under certain representational shifts.
