An Extended Tutorial and Vocabulary for Relational Language Design in an Era of AI-Assisted Query Generation

2026-08-12

AI Summary

This work addresses the challenges posed by the rise of AI-generated queries to the readability and structural explicitness of existing relational query languages. It proposes a unified framework based on Abstract Relational Calculus (ARC) and relational graphs to systematically compare how languages such as SQL, dataframes, and graph query notations express identical query intents. By introducing a formal terminology encompassing information needs, query mappings, and relational schema structures, the study for the first time brings classical database languages and emerging alternatives into a common analytical perspective. The framework is further extended to handle recursive queries, nested relations, and problems beyond PTIME. This contribution establishes a reusable language comparison methodology and a precise design lexicon, offering practical tools for evaluating and designing future relational query languages.

Abstract

Relational query languages have been studied and used for more than 50 years, with SQL dominant in practice. Today, queries are increasingly generated by machines and read by humans. At the same time, the landscape also includes dataframe, pipeline, logical, functional, graph, and relational programming notations. These developments invite two related questions beyond expressive power: which relational structures do languages make explicit, and how well can notation support users in reading and revising queries? This 3-hour tutorial extends an earlier SIGMOD'26 tutorial in three directions: recursive and path queries (connecting relational and graph query languages), nested relational data, and relational languages for problems beyond PTIME. Rather than beginning from formal definitions, we start from example queries and compare how different languages express the same intent. To compare recurring structure across notations, we use Abstract Relational Calculus (ARC) and Relational Diagrams as reference representations. From these examples, we develop a vocabulary for relational language design, including information need, query mapping, relational pattern structure, relational pattern denotation, and semantic conventions. Participants will leave with a framework for comparing existing and future relational languages, a precise vocabulary for articulating design trade-offs, and a concrete set of examples connecting classical database languages with alternative proposals.

Problem

• relational query languages
• query readability
• notation comparison
• relational structure
• AI-assisted query generation

Innovation

• relational language design
• AI-assisted query generation
• Abstract Relational Calculus
• relational diagrams
• query readability