  Query formulation is increasingly performed by systems that need to guess a
user's intent (e.g. via spoken word interfaces). But how can a user know that
the computational agent is returning answers to the "right" query? More
generally, given that relational queries can become pretty complicated, how can
we help users understand relational queries, whether human-generated or
automatically generated? Now seems the right moment to revisit a topic that
predates the birth of the relational model: developing visual metaphors that
help users understand relational queries.
  This lecture-style tutorial surveys the key visual metaphors developed for
diagrammatic representations of logical statements and relational expressions,
across both the relational database and the much older diagrammatic reasoning
communities. We survey the history and state-of-the-art of
relationally-complete diagrammatic representations of relational queries,
discuss the key visual metaphors developed in over a century of investigations
into diagrammatic languages, and organize the landscape by mapping the visual
alphabets of diagrammatic representation systems to the syntax and semantics of
Relational Algebra (RA) and Relational Calculus (RC). Tutorial website:
https://northeastern-datalab.github.io/diagrammatic-representation-tutorial/
