VISUEL: un tablero dinámico web para la visualización de datos

Data visualization aims to explore and analyze data quickly, interactively, and intuitively using visual representations. Faced with the constant growth of data in terms of volume and diversity, visualization techniques must confront the challenge of dealing with increasingly large datasets in terms of representation, interaction, and performance. Therefore, these techniques must be able to effectively convey the characteristics of the information space and inspire discovery. In this article, a web dynamic dashboard for data visualization called VISUEL is presented. VISUEL supports multiple coordinated views, integrating visualization techniques such as scatterplots, parallel coordinates, and box plots, and interactive schematic maps to represent information enriched with spatial references. VISUEL is fully interactive, supporting traditional interactions like filtering, selection, brushing and linking, and zooming, among others. It also allows the user to configure the visual representation of their data, by selecting the color and shape of the representations. The usefulness of this tool is illustrated using real-life dataset related to the wine industry in Argentina. Important aspects of the case study are discovered through the construction and analysis of multiple views.La visualización de datos tiene como objetivo explorar y analizar los datos de forma rápida, interactiva e intuitiva mediante representaciones visuales. Ante el constante crecimiento de los datos en términos de volumen y diversidad, las técnicas de visualización deben afrontar el desafío de lidiar con conjuntos de datos cada vez más grandes en términos de representación, interacción y desempeño. Por lo tanto, estas técnicas deben ser capaces de transmitir de manera efectiva las características del espacio de información e inspirar el descubrimiento. En este artículo, se presenta un tablero web dinámico para la visualización de datos llamado VISUEL. VISUEL admite múltiples vistas coordinadas, integrando técnicas de visualización como diagramas de dispersión, coordenadas paralelas, diagramas de caja, y mapas esquemáticos interactivos para representar información enriquecida con referencias espaciales. VISUEL es totalmente interactivo y admite interacciones tradicionales como filtrado, selección, brushing and linking, y zoom, entre otras. También permite al usuario configurar la representación visual de sus datos, seleccionando el color y la forma de las representaciones. La utilidad de esta herramienta se ilustra utilizando datos reales relacionados con la industria del vino en Argentina. Se descubren aspectos importantes del caso de estudio mediante la construcción y el análisis de múltiples vistas.Facultad de Informátic

Natural Language Interfaces for Tabular Data Querying and Visualization: A Survey

The emergence of natural language processing has revolutionized the way users interact with tabular data, enabling a shift from traditional query languages and manual plotting to more intuitive, language-based interfaces. The rise of large language models (LLMs) such as ChatGPT and its successors has further advanced this field, opening new avenues for natural language processing techniques. This survey presents a comprehensive overview of natural language interfaces for tabular data querying and visualization, which allow users to interact with data using natural language queries. We introduce the fundamental concepts and techniques underlying these interfaces with a particular emphasis on semantic parsing, the key technology facilitating the translation from natural language to SQL queries or data visualization commands. We then delve into the recent advancements in Text-to-SQL and Text-to-Vis problems from the perspectives of datasets, methodologies, metrics, and system designs. This includes a deep dive into the influence of LLMs, highlighting their strengths, limitations, and potential for future improvements. Through this survey, we aim to provide a roadmap for researchers and practitioners interested in developing and applying natural language interfaces for data interaction in the era of large language models.Comment: 20 pages, 4 figures, 5 tables. Submitted to IEEE TKD

Propagating Visual Designs to Numerous Plots and Dashboards

In the process of developing an infrastructure for providing visualization and visual analytics (VIS) tools to epidemiologists and modeling scientists, we encountered a technical challenge for applying a number of visual designs to numerous datasets rapidly and reliably with limited development resources. In this paper, we present a technical solution to address this challenge. Operationally, we separate the tasks of data management, visual designs, and plots and dashboard deployment in order to streamline the development workflow. Technically, we utilize: an ontology to bring datasets, visual designs, and deployable plots and dashboards under the same management framework; multi-criteria search and ranking algorithms for discovering potential datasets that match a visual design; and a purposely-design user interface for propagating each visual design to appropriate datasets (often in tens and hundreds) and quality-assuring the propagation before the deployment. This technical solution has been used in the development of the RAMPVIS infrastructure for supporting a consortium of epidemiologists and modeling scientists through visualization

A visual analytics system for neuronal morphology analysis of zebrafish brain

Department of Computer Science and EngineeringThe electron microscope is capable of capturing tissue in three dimensions up to the nanometer scale. This makes it possible to analyze the shape of brain cells beyond conventional analysis throughout the brain region. Morphological analysis of brain regions requires tracking the morphological features of each cell and applying techniques for visualizing electronic microscope (EM) volume data and brain cells at the same time, as well as brain region information such as diencephalon and mesencephalon. However, morphological analysis of cells in the brain region has been challenging. Because terabytes of EM volume data must be processed, brain cell segmentation and accurate brain subregion information down to 120 nm are required. In this thesis, I present a novel system for visualizing and analyzing EM volume data and extracted brain cell regions. The system shows an EM volume data view and a histogram of the morphological features, and it provides a graph-based user interface. This system classifies the whole brain cell cluster into various brain cell selection methods according to the type and expresses statistical information of the selected cell cluster. This allows the user to mask only the desired brain regions or desired brain cells and to perform a morphological analysis of the brain cell population. Using this system, users can quickly analyze the brain cell type and the role of the group by using the user interface. By using this system, I can know the shape of the brain cell by brain area and analyze brain cell morphology and function by known brain area function. I also propose an efficient data structure of terabytes of data to construct a system. I performed several case studies, including zebrafish whole brain cells analysis, diencephalon and habenula analysis, mesencephalon tectum neuropil analysis, and optic nerve analysis with brain expert using 120 nm/px isotropic zebrafish EM data.clos

DesignSense: A Visual Analytics Interface for Navigating Generated Design Spaces

Generative Design (GD) produces many design alternatives and promises novel and performant solutions to architectural design problems. The success of GD rests on the ability to navigate the generated alternatives in a way that is unhindered by their number and in a manner that reflects design judgment, with its quantitative and qualitative dimensions. I address this challenge by critically analyzing the literature on design space navigation (DSN) tools through a set of iteratively developed lenses. The lenses are informed by domain experts\u27 feedback and behavioural studies on design navigation under choice-overload conditions. The lessons from the analysis shaped DesignSense, which is a DSN tool that relies on visual analytics techniques for selecting, inspecting, clustering and grouping alternatives. Furthermore, I present case studies of navigating realistic GD datasets from architecture and game design. Finally, I conduct a formative focus group evaluation with design professionals that shows the tool\u27s potential and highlights future directions