  Inspired by the visualization of dental plaque at the dentist's office, this
article proposes a novel visualization technique for identifying redundancies
in relational data. Our approach builds upon an established
information-theoretic framework that, despite being well-principled, remains
unexplored in practical applications. In this framework, we calculate the
information content (or entropy) of each cell in a relation instance, given a
set of functional dependencies. The entropy value represents the likelihood of
inferring the cell's value based on the dependencies and the remaining tuples.
By highlighting cells with lower entropy, we effectively visualize redundancies
in the data. We present an initial prototype implementation and demonstrate
that a straightforward approach is insufficient for handling practical problem
sizes. To address this limitation, we propose several optimizations, which we
prove to be correct. Additionally, we present a Monte Carlo approximation
technique with a known error, enabling computationally tractable computations.
Using a real-world dataset of modest size, we illustrate the potential of our
visualization technique. Our vision is to support domain experts with data
profiling and data cleaning tasks, akin to the functionality of a plaque test
at the dentist's.
