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Nathan Selikoff: Explorations in Higher Dimensionality and Complexity

ieee COMPUTER GRAPHICS AND APPLICATIONS 2023年第5期43卷 122-130页

作者： Selikoff, Nathan Campbell, Bruce D. Samsel, Francesca Data Visualizat & UI Engn Artist Founder Orlando FL 32801 USA Rhode Isl Sch Design Providence RI 02903 USA Univ Texas Texas Adv Comp Ctr Austin TX 78712 USA

While data scientists pursue new extremes of high-dimensional data, and data visualization professionals attempt to visualize within that space, Nathan Selikoff's work suggested a unique perspective on relationship among complexity, higher dimensions, and sensing what otherwise cannot be sensed. His long-tailed pursuit of visualizing complexity piqued our interest as we interviewed him for this article.

关键词： data visualization Complexity theory Sensors data science

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Federated visualization: A Privacy-Preserving Strategy for Aggregated Visual Query

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ieee TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2023年第6期29卷 2901-2913页

作者： Chen, Wei Wei, Yating Wang, Zhiyong Zhou, Shuyue Lin, Bingru Zhou, Zhiguang Zhejiang Univ State Key Lab CAD & CG Hangzhou 310058 Zhejiang Peoples R China Zhejiang Met & Mat Co Hangzhou 310003 Zhejiang Peoples R China Tencent Guangzhou 510630 Peoples R China Alibaba Grp Hangzhou 310058 Zhejiang Peoples R China Hangzhou Dianzi Univ Hangzhou 310018 Zhejiang Peoples R China

We present a novel privacy preservation strategy for aggregated visual query of decentralized data. The key idea is to imitate the flowchart of the federated learning framework, and reformulate the visualization process within a federated infrastructure. The federation of visualization is fulfilled by leveraging a shared global module that composes the encrypted externalizations of transformed visual features of data pieces in local modules. We design two implementations of federated visualization: a prediction-based scheme, and a query-based scheme. We demonstrate the effectiveness of our approach with a set of visual forms, and verify its robustness with evaluations. We report the value of federated visualization in real scenarios with an expert review.

关键词： data visualization visualization data privacy Privacy data models Federated learning Servers Decentralized visualization federated visualization privacy-preserving visualization

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The Stories We Tell About data: Surveying data-Driven Storytelling Using visualization

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ieee COMPUTER GRAPHICS AND APPLICATIONS 2023年第4期43卷 97-110页

作者： Zhao, Zhenpeng Elmqvist, Niklas Apple Inc Cupertino CA 95014 USA Univ Maryland College Pk MD 20742 USA

The emerging practice of data-driven storytelling is framing data using familiar narrative mechanisms, such as slideshows, videos, and comics, to make even highly complex phenomena understandable. In this survey, we propose a taxonomy focused specifically on media types for the purpose of widening the purview of data-driven storytelling by putting more tools into the hands of designers. The classification shows that current data-driven storytelling practice does not yet leverage the full repertoire of media that can be used for storytelling, such as the spoken word, e-learning, and video games. Using our taxonomy as a generative tool, we also explore three novel storytelling mechanisms, including for live streaming, gesture-driven oral presentations, and data-driven comics.

关键词： data visualization Media visualization Taxonomy Video games Source coding Electronic learning Streaming media

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Visual data Exploration as a Statistical Testing Procedure: Within-View and Between-View Multiple Comparisons

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ieee TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2023年第9期29卷 3937-3948页

作者： Savvides, Rafael Henelius, Andreas Oikarinen, Emilia Puolamaki, Kai Univ Helsinki Helsinki Finland

A fundamental problem in visual data exploration concerns whether observed patterns are true or merely random noise. This problem is especially pertinent in visual analytics, where the user is presented with a barrage of patterns, without any guarantees of their statistical validity. Recently this problem has been formulated in terms of statistical testing and the multiple comparisons problem. In this paper, we identify two levels of multiple comparisons problems in visualization: the within-view and the between-view problem. We develop a statistical testing procedure for interactive data exploration that controls the family-wise error rate on both levels. The procedure enables the user to determine the compatibility of their assumptions about the data with visually observed patterns. We present use-cases where we visualize and evaluate patterns in real-world data.

关键词： Information visualization interactive data exploration and discovery statistical testing

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Curio: A dataflow-Based Framework for Collaborative Urban Visual Analytics

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ieee TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2025年第1期31卷 1224-1234页

作者： Moreira, Gustavo Hosseini, Maryam Veiga, Carolina Alexandre, Lucas Colaninno, Nicola de Oliveira, Daniel Ferreira, Nivan Lage, Marcos Miranda, Fabio Univ Illinois Chicago IL 60607 USA Univ Calif Berkeley Berkeley CA USA MIT Cambridge MA USA Univ Illinois Champaign IL USA Univ Fed Fluminense Niteroi Brazil Politecn Milan Milan Italy Univ Fed Pernambuco Recife Brazil

Over the past decade, several urban visual analytics systems and tools have been proposed to tackle a host of challenges faced by cities, in areas as diverse as transportation, weather, and real estate. Many of these tools have been designed through collaborations with urban experts, aiming to distill intricate urban analysis workflows into interactive visualizations and interfaces. However, the design, implementation, and practical use of these tools still rely on siloed approaches, resulting in bespoke systems that are difficult to reproduce and extend. At the design level, these tools undervalue rich data workflows from urban experts, typically treating them only as data providers and evaluators. At the implementation level, they lack interoperability with other technical frameworks. At the practical use level, they tend to be narrowly focused on specific fields, inadvertently creating barriers to cross-domain collaboration. To address these gaps, we present Curio, a framework for collaborative urban visual analytics. Curio uses a dataflow model with multiple abstraction levels (code, grammar, GUI elements) to facilitate collaboration across the design and implementation of visual analytics components. The framework allows experts to intertwine data preprocessing, management, and visualization stages while tracking the provenance of code and visualizations. In collaboration with urban experts, we evaluate Curio through a diverse set of usage scenarios targeting urban accessibility, urban microclimate, and sunlight access. These scenarios use different types of data and domain methodologies to illustrate Curio's flexibility in tackling pressing societal challenges. Curio is available at ***/curio.

关键词： data visualization Visual analytics Collaboration Three-dimensional displays Urban areas Codes Pipelines Urban analytics urban data spatial data dataflow provenance visualization framework visualization system

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Deep Clustering and visualization for End-to-End High-Dimensional data Analysis

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ieee TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023年第11期34卷 8543-8554页

作者： Wu, Lirong Yuan, Lifan Zhao, Guojiang Lin, Haitao Li, Stan Z. Westlake Univ Sch Engn AI Lab Hangzhou 310000 Peoples R China

High-dimensional data analysis for exploration and discovery includes two fundamental tasks: deep clustering and data visualization. When these two associated tasks are done separately, as is often the case thus far, disagreements can occur among the tasks in terms of geometry preservation. Namely, the clustering process is often accompanied by the corruption of the geometric structure, whereas visualization aims to preserve the data geometry for better interpretation. Therefore, how to achieve deep clustering and data visualization in an end-to-end unified framework is an important but challenging problem. In this article, we propose a novel neural network-based method, called deep clustering and visualization (DCV), to accomplish the two associated tasks end-to-end to resolve their disagreements. The DCV framework consists of two nonlinear dimensionality reduction (NLDR) transformations: 1) one from the input data space to latent feature space for clustering and 2) the other from the latent feature space to the final 2-D space for visualization. Importantly, the first NLDR transformation is mainly optimized by one Clustering Loss, allowing arbitrary corruption of the geometric structure for better clustering, while the second NLDR transformation is optimized by one Geometry-Preserving Loss to recover the corrupted geometry for better visualization. Extensive comparative results show that the DCV framework outperforms other leading clustering-visualization algorithms in terms of both quantitative evaluation metrics and qualitative visualization.

关键词： data visualization Geometry Clustering algorithms Task analysis data analysis Neural networks Manifolds data visualization deep clustering geometry preservation neural networks unsupervised learning

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CoordNet: data Generation and visualization Generation for Time-Varying Volumes via a Coordinate-Based Neural Network

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ieee TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2023年第12期29卷 4951-4963页

作者： Han, Jun Wang, Chaoli Chinese Univ Hong Kong Sch Data Sci Shenzhen 518172 Guangdong Peoples R China Univ Notre Dame Dept Comp Sci & Engn Notre Dame IN 46556 USA

Although deep learning has demonstrated its capability in solving diverse scientific visualization problems, it still lacks generalization power across different tasks. To address this challenge, we propose CoordNet, a single coordinate-based framework that tackles various tasks relevant to time-varying volumetric data visualization without modifying the network architecture. The core idea of our approach is to decompose diverse task inputs and outputs into a unified representation (i.e., coordinates and values) and learn a function from coordinates to their corresponding values. We achieve this goal using a residual block-based implicit neural representation architecture with periodic activation functions. We evaluate CoordNet on data generation (i.e., temporal super-resolution and spatial super-resolution) and visualization generation (i.e., view synthesis and ambient occlusion prediction) tasks using time-varying volumetric data sets of various characteristics. The experimental results indicate that CoordNet achieves better quantitative and qualitative results than the state-of-the-art approaches across all the evaluated tasks. Source code and pre-trained models are available at https://***/stevenhan1991/CoordNet.

关键词： Task analysis data visualization Rendering (computer graphics) Deep learning Superresolution Neural networks Decoding Volume visualization implicit neural representation data generation visualization generation

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Sensemaking Sans Power: Interactive data visualization Using Color-Changing Ink

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ieee TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2023年第12期29卷 5282-5293页

作者： Patnaik, Biswaksen Peng, Huaishu Elmqvist, Niklas Univ Maryland College Pk MD 20742 USA

We present an approach for interactively visualizing data using color-changing inks without the need for electronic displays or computers. Color-changing inks are a family of physical inks that change their color characteristics in response to an external stimulus such as heat, UV light, water, and pressure. visualizations created using color-changing inks can embed interactivity in printed material without external computational media. In this article, we survey current color-changing ink technology and then use these findings to derive a framework for how it can be used to construct interactive data representations. We also enumerate the interaction techniques possible using this technology. We then show some examples of how to use color-changing ink to create interactive visualizations on paper. While obviously limited in scope to situations where no power or computing is present, or as a complement to digital displays, our findings can be employed for paper, data physicalization, and embedded visualizations.

关键词： Physical computing color-changing inks design space data physicalization data visualization

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A Framework for Multiclass Contour visualization

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ieee Transactions on visualization and Computer Graphics 2023年第1期29卷 353-362页

作者： Li, Sihang Yu, Jiacheng Li, Mingxuan Liu, Le Zhang, Xiaolong Luke Yuan, Xiaoru Laboratory of Machine Perception Ministry of Education School of AI Peking University China Center for Data Science Academy for Advanced Interdisciplinary Studies Peking University China National Engineering Laboratory for Big Data Analysis and Application Peking University China School of Computer Science Northwestern Polytechnical University China College of Information Sciences and Technology Pennsylvania State University United States

Multiclass contour visualization is often used to interpret complex data attributes in such fields as weather forecasting, computational fluid dynamics, and artificial intelligence. However, effective and accurate representations of underlying data patterns and correlations can be challenging in multiclass contour visualization, primarily due to the inevitable visual cluttering and occlusions when the number of classes is significant. To address this issue, visualization design must carefully choose design parameters to make visualization more comprehensible. With this goal in mind, we proposed a framework for multiclass contour visualization. The framework has two components: a set of four visualization design parameters, which are developed based on an extensive review of literature on contour visualization, and a declarative domain-specific language (DSL) for creating multiclass contour rendering, which enables a fast exploration of those design parameters. A task-oriented user study was conducted to assess how those design parameters affect users' interpretations of real-world data. The study results offered some suggestions on the value choices of design parameters in multiclass contour visualization. © 2022 ieee.

关键词： data visualization

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IDLat: An Importance-Driven Latent Generation Method for Scientific data

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ieee Transactions on visualization and Computer Graphics 2023年第1期29卷 679-689页

作者： Shen, Jingyi Li, Haoyu Xu, Jiayi Biswas, Ayan Shen, Han-Wei Department of Computer Science and Engineering The Ohio State University United States Los Alamos National Laboratory United States

Deep learning based latent representations have been widely used for numerous scientific visualization applications such as isosurface similarity analysis, volume rendering, flow field synthesis, and data reduction, just to name a few. However, existing latent representations are mostly generated from raw data in an unsupervised manner, which makes it difficult to incorporate domain interest to control the size of the latent representations and the quality of the reconstructed data. In this paper, we present a novel importance-driven latent representation to facilitate domain-interest-guided scientific data visualization and analysis. We utilize spatial importance maps to represent various scientific interests and take them as the input to a feature transformation network to guide latent generation. We further reduced the latent size by a lossless entropy encoding algorithm trained together with the autoencoder, improving the storage and memory efficiency. We qualitatively and quantitatively evaluate the effectiveness and efficiency of latent representations generated by our method with data from multiple scientific visualization applications. © 2022 ieee.

关键词： data visualization

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