QueryVis: Logic-based diagrams help users understand complicated SQL queries faster
| Autor: | Aristotelis Leventidis, Wolfgang Gatterbauer, Jiahui Zhang, Cody Dunne, Mirek Riedewald, H. V. Jagadish |
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| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
SQL Computer Science - Logic in Computer Science Computer science Semantics (computer science) media_common.quotation_subject Computer Science - Human-Computer Interaction 02 engineering and technology computer.software_genre Human-Computer Interaction (cs.HC) Computer Science - Databases Reading (process) Schema (psychology) 0202 electrical engineering electronic engineering information engineering Code (cryptography) 0501 psychology and cognitive sciences 050107 human factors media_common computer.programming_language Programming language 05 social sciences InformationSystems_DATABASEMANAGEMENT 020207 software engineering Databases (cs.DB) First-order logic Visualization Logic in Computer Science (cs.LO) Diagrammatic reasoning Conjunctive query computer |
| Zdroj: | SIGMOD Conference |
| DOI: | 10.48550/arxiv.2004.11375 |
| Popis: | Understanding the meaning of existing SQL queries is critical for code maintenance and reuse. Yet SQL can be hard to read, even for expert users or the original creator of a query. We conjecture that it is possible to capture the logical intent of queries in \emph{automatically-generated visual diagrams} that can help users understand the meaning of queries faster and more accurately than SQL text alone. We present initial steps in that direction with visual diagrams that are based on the first-order logic foundation of SQL and can capture the meaning of deeply nested queries. Our diagrams build upon a rich history of diagrammatic reasoning systems in logic and were designed using a large body of human-computer interaction best practices: they are \emph{minimal} in that no visual element is superfluous; they are \emph{unambiguous} in that no two queries with different semantics map to the same visualization; and they \emph{extend} previously existing visual representations of relational schemata and conjunctive queries in a natural way. An experimental evaluation involving 42 users on Amazon Mechanical Turk shows that with only a 2--3 minute static tutorial, participants could interpret queries meaningfully faster with our diagrams than when reading SQL alone. Moreover, we have evidence that our visual diagrams result in participants making fewer errors than with SQL. We believe that more regular exposure to diagrammatic representations of SQL can give rise to a \emph{pattern-based} and thus more intuitive use and re-use of SQL. All details on the experimental study, the evaluation stimuli, raw data, and analyses, and source code are available at https://osf.io/mycr2 Comment: Full version of paper appearing in SIGMOD 2020 |
| Databáze: | OpenAIRE |
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