Principles of Query Visualization

Query Visualization (QV) is the problem of transforming a given query into a graphical representation that helps humans understand its meaning. This task is notably different from designing a Visual Query Language (VQL) that helps a user compose a query. This article discusses the principles of relational query visualization and its potential for simplifying user interactions with relational data.Comment: 20 pages, 12 figures, preprint for IEEE Data Engineering Bulleti

A Tutorial on Visual Representations of Relational Queries

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 that help users understand relational queries. 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).Comment: 4 page tutorial paper at VLDB 2023, tutorial web page with slides to be posted in time: https://northeastern-datalab.github.io/visual-query-representation-tutorial/. arXiv admin note: text overlap with arXiv:2208.0161

Visualizing Statistical Analysis of Multi Tabular Attributes with SQL

Data extraction and data management is playing a vital role in today’s world. Databases are widely used by all the organizations. Analysis of data is very crucial when comparisons are done between different subjects. There are many software’s developed for statistical analysis of data. Various visualization techniques are used for representation. In statistical analysis of tabular data in databases, data is either extracted as external sheets or the statistical software’s are connected to the servers to test data. In our approach, we introduce a web based interface where users can select any number of attributes and view the results with some simple visualizations. SQL queries are written for different methodologies to analyze data. Formulas and structure of all the queries are visualized and represented for the users to understand the query processing and the test methodologies. All the statistical tests are performed on multi tabular data. Ranking is performed on categorical data to replace these values with ranks. With the selected attributes, views are created in the database with the ranks replacing the categorical values in these views. The developed interface is tested with different users to evaluate the visualizations used and the understandability of the statistical tests.Computer Scienc

Desarrollo de una notación y herramienta de visualización de datos para mejorar la capacidad de análisis de procedimientos almacenados SQL

Proyecto de Graduación (Maestría en Ingeniería en Computación) Instituto Tecnológico de Costa Rica, Escuela de Ingeniería en Computación, 2017.The digital age in which we live is framed by a vertiginous development of both hardware and software, which means that in most of the companies the production processes are controlled by computer systems that every day support more operations and therefore become more complex, at the same time, advances in the storage capacity of computers allows companies to store more data, which are used for decision making. These data require technologies that allow efficient processing of information, which has made database technologies very important technologies for companies and also the SQL (Structured Query Language) language for query and data manipulation. Nowadays it is common to find much of the business logic of companies written in SQL language (e.g. stored procedures). The maintenance and evolution of the SQL code is a complex and expensive task for the companies because, among other factors, it takes time for a person to understand code already written and to be able to modify it without this having repercussions in other stored procedures. Therefore, the objective of this work has been to increase the ability to analyze SQL code and also reduce the time required to understand SQL statements taking advantage of the human brain's ability to interpret visual information in parallel, something that is not possible with the text which is processed sequentially. To achieve the above objective the present research focused on: a) creating a visual notation capable of graphically representing SQL code instructions, b) developing a tool that uses this notation to visualize SQL stored procedures, and c) validate that the use of visual notation saves time with regards to understanding code in text. Visual notation was designed considering principles that maximize cognitive effectiveness (i.e. speed, ease and precision with which information can be extracted), namely: semiotic clarity, perceptive discrimination, perceptual immediacy, visual expressiveness and graphic parsimony between others

QueryVis: Logic-based diagrams help users understand complicated SQL queries faster

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/mycr2Comment: Full version of paper appearing in SIGMOD 202

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