A Comprehensive Tutorial on over 100 Years of Diagrammatic Representations of Logical Statements and Relational Queries
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arXiv
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866913292093489152 |
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| author | Gatterbauer, Wolfgang |
| author_facet | Gatterbauer, Wolfgang |
| contents | 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/ |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_00007 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Comprehensive Tutorial on over 100 Years of Diagrammatic Representations of Logical Statements and Relational Queries Gatterbauer, Wolfgang Databases Logic in Computer Science 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/ |
| title | A Comprehensive Tutorial on over 100 Years of Diagrammatic Representations of Logical Statements and Relational Queries |
| topic | Databases Logic in Computer Science |
| url | https://arxiv.org/abs/2404.00007 |