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 existing 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 of relational queries.

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This lecture-style tutorial surveys the key visual metaphors developed for visual representations of relational expressions. We will 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 investigating diagrammatic languages, and organize the landscape by mapping their used visual alphabets to the syntax and semantics of Relational Algebra (RA) and Relational Calculus (RC).

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Slides

Visual Representations of Relational Queries (VLDB 2023 tutorial)

Feedback

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Funding

This work has been supported in part by the National Science Foundation (NSF) under award numbers IIS-1762268 and IIS-1956096, and conducted in part while the author was visiting the Simons Institute for the Theory of Computing. Any opinions, findings, and conclusions or recommendations expressed in this project are those of the author(s) and do not necessarily reflect the views of the Funding Agencies.

National Science Foundation Simons Institute for the Theory of Computing

Reference

A Tutorial on Visual Representations of Relational Queries
Wolfgang Gatterbauer
PVLDB, 16(12), pp. 3890-3893, 2023
DOI | PVLDB | arXiv:2308.10319 | Tutorial slides | gs

To cite this tutorial, please use following bibtex entry:

@article{VLDBtutorial:2023:VisualRepresentations,
  author = {Wolfgang Gatterbauer},
  title = {A Tutorial on Visual Representations of Relational Queries},
  journal = {PVLDB},
  volume = {16},
  number = {12}
  pages = {3890-3893},
  year = {2023},
  doi = {10.14778/3611540.3611578}
}