DevLan/vispubs huggingface dataset:
InvVis: Large-Scale Data Embedding for Invertible Visualization
Authors: Huayuan Ye, Chenhui Li, Yang Li, Changbo Wang
Published at Vis, 2023

We present InvVis, a new approach for invertible visualization, which is reconstructing or further modifying a visualization from an image. InvVis allows the embedding of a significant amount of data, such as chart data, chart information, source code, etc., into visualization images. The encoded image is perceptually indistinguishable from the original one. We propose a new method to efficiently express chart data in the form of images, enabling large-capacity data embedding. We also outline a model based on the invertible neural network to achieve high-quality data concealing and revealing. We explore and implement a variety of application scenarios of InvVis. Additionally, we conduct a series of evaluation experiments to assess our method from multiple perspectives, including data embedding quality, data restoration accuracy, data encoding capacity, etc. The result of our experiments demonstrates the great potential of InvVis in invertible visualization.
Knowledge Graphs in Practice: Characterizing their Users, Challenges, and Visualization Opportunities
Authors: Harry X. Li, Gabriel Appleby, Camelia Daniela Brumar, Remco Chang, Ashley Suh 0001
Published at Vis, 2023

This study presents insights from interviews with nineteen Knowledge Graph (KG) practitioners who work in both enterprise and academic settings on a wide variety of use cases. Through this study, we identify critical challenges experienced by KG practitioners when creating, exploring, and analyzing KGs that could be alleviated through visualization design. Our findings reveal three major personas among KG practitioners – KG Builders, Analysts, and Consumers – each of whom have their own distinct expertise and needs. We discover that KG Builders would benefit from schema enforcers, while KG Analysts need customizable query builders that provide interim query results. For KG Consumers, we identify a lack of efficacy for node-link diagrams, and the need for tailored domain-specific visualizations to promote KG adoption and comprehension. Lastly, we find that implementing KGs effectively in practice requires both technical and social solutions that are not addressed with current tools, technologies, and collaborative workflows. From the analysis of our interviews, we distill several visualization research directions to improve KG usability, including knowledge cards that balance digestibility and discoverability, timeline views to track temporal changes, interfaces that support organic discovery, and semantic explanations for AI and machine learning predictions.
Large-Scale Evaluation of Topic Models and Dimensionality Reduction Methods for 2D Text Spatialization
Authors: Daniel Atzberger, Tim Cech, Matthias Trapp 0001, Rico Richter, Willy Scheibel, Jürgen Döllner, Tobias Schreck
Published at Vis, 2023

Topic models are a class of unsupervised learning algorithms for detecting the semantic structure within a text corpus. Together with a subsequent dimensionality reduction algorithm, topic models can be used for deriving spatializations for text corpora as two-dimensional scatter plots, reflecting semantic similarity between the documents and supporting corpus analysis. Although the choice of the topic model, the dimensionality reduction, and their underlying hyperparameters significantly impact the resulting layout, it is unknown which particular combinations result in high-quality layouts with respect to accuracy and perception metrics. To investigate the effectiveness of topic models and dimensionality reduction methods for the spatialization of corpora as two-dimensional scatter plots (or basis for landscape-type visualizations), we present a large-scale, benchmark-based computational evaluation. Our evaluation consists of (1) a set of corpora, (2) a set of layout algorithms that are combinations of topic models and dimensionality reductions, and (3) quality metrics for quantifying the resulting layout. The corpora are given as document-term matrices, and each document is assigned to a thematic class. The chosen metrics quantify the preservation of local and global properties and the perceptual effectiveness of the two-dimensional scatter plots. By evaluating the benchmark on a computing cluster, we derived a multivariate dataset with over 45 000 individual layouts and corresponding quality metrics. Based on the results, we propose guidelines for the effective design of text spatializations that are based on topic models and dimensionality reductions. As a main result, we show that interpretable topic models are beneficial for capturing the structure of text corpora. We furthermore recommend the use of t-SNE as a subsequent dimensionality reduction.
Let the Chart Spark: Embedding Semantic Context into Chart with Text-to-Image Generative Model
Authors: Shishi Xiao, Suizi Huang, Yue Lin, Yilin Ye, Wei Zeng
Published at Vis, 2023

Pictorial visualization seamlessly integrates data and semantic context into visual representation, conveying complex information in an engaging and informative manner. Extensive studies have been devoted to developing authoring tools to simplify the creation of pictorial visualizations. However, mainstream works follow a retrieving-and-editing pipeline that heavily relies on retrieved visual elements from a dedicated corpus, which often compromise data integrity. Text-guided generation methods are emerging, but may have limited applicability due to their predefined entities. In this work, we propose ChartSpark, a novel system that embeds semantic context into chart based on text-to-image generative models. ChartSpark generates pictorial visualizations conditioned on both semantic context conveyed in textual inputs and data information embedded in plain charts. The method is generic for both foreground and background pictorial generation, satisfying the design practices identified from empirical research into existing pictorial visualizations. We further develop an interactive visual interface that integrates a text analyzer, editing module, and evaluation module to enable users to generate, modify, and assess pictorial visualizations. We experimentally demonstrate the usability of our tool, and conclude with a discussion of the potential of using text-to-image generative models combined with an interactive interface for visualization design.
Leveraging Historical Medical Records as a Proxy via Multimodal Modeling and Visualization to Enrich Medical Diagnostic Learning
Authors: Yang Ouyang, Yuchen Wu, He Wang, Chenyang Zhang, Furui Cheng, Chang Jiang, Lixia Jin, Yuanwu Cao, Quan Li
Published at Vis, 2023

Simulation-based Medical Education (SBME) has been developed as a cost-effective means of enhancing the diagnostic skills of novice physicians and interns, thereby mitigating the need for resource-intensive mentor-apprentice training. However, feedback provided in most SBME is often directed towards improving the operational proficiency of learners, rather than providing summative medical diagnoses that result from experience and time. Additionally, the multimodal nature of medical data during diagnosis poses significant challenges for interns and novice physicians, including the tendency to overlook or over-rely on data from certain modalities, and difficulties in comprehending potential associations between modalities. To address these challenges, we present DiagnosisAssistant, a visual analytics system that leverages historical medical records as a proxy for multimodal modeling and visualization to enhance the learning experience of interns and novice physicians. The system employs elaborately designed visualizations to explore different modality data, offer diagnostic interpretive hints based on the constructed model, and enable comparative analyses of specific patients. Our approach is validated through two case studies and expert interviews, demonstrating its effectiveness in enhancing medical training.
LiberRoad: Probing into the Journey of Chinese Classics Through Visual Analytics
Authors: Yuhan Guo, Yuchu Luo, Keer Lu, Linfang Li, Haizheng Yang, Xiaoru Yuan
Published at Vis, 2023

Books act as a crucial carrier of cultural dissemination in ancient times. This work involves joint efforts between visualization and humanities researchers, aiming at building a holistic view of the cultural exchange and integration between China and Japan brought about by the overseas circulation of Chinese classics. Book circulation data consist of uncertain spatiotemporal trajectories, with multiple dimensions, and movement across hierarchical spaces forms a compound network. LiberRoad visualizes the circulation of books collected in the Imperial Household Agency of Japan, and can be generalized to other book movement data. The LiberRoad system enables a smooth transition between three views (Location Graph, map, and timeline) according to the desired perspectives (spatial or temporal), as well as flexible filtering and selection. The Location Graph is a novel uncertainty-aware visualization method that employs improved circle packing to represent spatial hierarchy. The map view intuitively shows the overall circulation by clustering and allows zooming into single book trajectory with lenses magnifying local movements. The timeline view ranks dynamically in response to user interaction to facilitate the discovery of temporal events. The evaluation and feedback from the expert users demonstrate that LiberRoad is helpful in revealing movement patterns and comparing circulation characteristics of different times and spaces.
LiveRetro: Visual Analytics for Strategic Retrospect in Livestream E-Commerce
Authors: Yuchen Wu, Yuansong Xu, Shenghan Gao, Xingbo Wang 0001, Wenkai Song, Zhiheng Nie, Xiaomeng Fan, Quan Li
Published at Vis, 2023

Livestream e-commerce integrates live streaming and online shopping, allowing viewers to make purchases while watching. However, effective marketing strategies remain a challenge due to limited empirical research and subjective biases from the absence of quantitative data. Current tools fail to capture the interdependence between live performances and feedback. This study identified computational features, formulated design requirements, and developed LiveRetro, an interactive visual analytics system. It enables comprehensive retrospective analysis of livestream e-commerce for streamers, viewers, and merchandise. LiveRetro employs enhanced visualization and time-series forecasting models to align performance features and feedback, identifying influences at channel, merchandise, feature, and segment levels. Through case studies and expert interviews, the system provides deep insights into the relationship between live performance and streaming statistics, enabling efficient strategic analysis from multiple perspectives.
ManiVault: A Flexible and Extensible Visual Analytics Framework for High-Dimensional Data
Authors: Alexander Vieth, Thomas Kroes, Julian Thijssen, Baldur van Lew, Jeroen Eggermont, Soumyadeep Basu, Elmar Eisemann, Anna Vilanova, Thomas Höllt, Boudewijn P. F. Lelieveldt
Published at Vis, 2023

Exploration and analysis of high-dimensional data are important tasks in many fields that produce large and complex data, like the financial sector, systems biology, or cultural heritage. Tailor-made visual analytics software is developed for each specific application, limiting their applicability in other fields. However, as diverse as these fields are, their characteristics and requirements for data analysis are conceptually similar. Many applications share abstract tasks and data types and are often constructed with similar building blocks. Developing such applications, even when based mostly on existing building blocks, requires significant engineering efforts. We developed ManiVault, a flexible and extensible open-source visual analytics framework for analyzing high-dimensional data. The primary objective of ManiVault is to facilitate rapid prototyping of visual analytics workflows for visualization software developers and practitioners alike. ManiVault is built using a plugin-based architecture that offers easy extensibility. While our architecture deliberately keeps plugins self-contained, to guarantee maximum flexibility and re-usability, we have designed and implemented a messaging API for tight integration and linking of modules to support common visual analytics design patterns. We provide several visualization and analytics plugins, and ManiVault's API makes the integration of new plugins easy for developers. ManiVault facilitates the distribution of visualization and analysis pipelines and results for practitioners through saving and reproducing complete application states. As such, ManiVault can be used as a communication tool among researchers to discuss workflows and results. A copy of this paper and all supplemental material is available at osf.io/9k6jw, and source code at github.com/ManiVaultStudio.
Marjorie: Visualizing Type 1 Diabetes Data to Support Pattern Exploration
Authors: Anna Scimone, Klaus Eckelt, Marc Streit, Andreas P. Hinterreiter
Published at Vis, 2023

In this work we propose Marjorie, a visual analytics approach to address the challenge of analyzing patients' diabetes data during brief regular appointments with their diabetologists. Designed in consultation with diabetologists, Marjorie uses a combination of visual and algorithmic methods to support the exploration of patterns in the data. Patterns of interest include seasonal variations of the glucose profiles, and non-periodic patterns such as fluctuations around mealtimes or periods of hypoglycemia (i.e., glucose levels below the normal range). We introduce a unique representation of glucose data based on modified horizon graphs and hierarchical clustering of adjacent carbohydrate or insulin entries. Semantic zooming allows the exploration of patterns on different levels of temporal detail. We evaluated our solution in a case study, which demonstrated Marjorie's potential to provide valuable insights into therapy parameters and unfavorable eating habits, among others. The study results and informal feedback collected from target users suggest that Marjorie effectively supports patients and diabetologists in the joint exploration of patterns in diabetes data, potentially enabling more informed treatment decisions. A free copy of this paper and all supplemental materials are available at https://osf.io/34t8c/.
Merge Tree Geodesics and Barycenters with Path Mappings
Authors: Florian Wetzels, Mathieu Pont, Julien Tierny, Christoph Garth
Published at Vis, 2023

Comparative visualization of scalar fields is often facilitated using similarity measures such as edit distances. In this paper, we describe a novel approach for similarity analysis of scalar fields that combines two recently introduced techniques: Wasserstein geodesics/barycenters as well as path mappings, a branch decomposition-independent edit distance. Effectively, we are able to leverage the reduced susceptibility of path mappings to small perturbations in the data when compared with the original Wasserstein distance. Our approach therefore exhibits superior performance and quality in typical tasks such as ensemble summarization, ensemble clustering, and temporal reduction of time series, while retaining practically feasible runtimes. Beyond studying theoretical properties of our approach and discussing implementation aspects, we describe a number of case studies that provide empirical insights into its utility for comparative visualization, and demonstrate the advantages of our method in both synthetic and real-world scenarios. We supply a C++ implementation that can be used to reproduce our results.
MeTACAST: Target- and Context-Aware Spatial Selection in VR
Authors: Lixiang Zhao, Tobias Isenberg 0001, Fuqi Xie, Hai-Ning Liang, Lingyun Yu 0001
Published at Vis, 2023

We propose three novel spatial data selection techniques for particle data in VR visualization environments. They are designed to be target- and context-aware and be suitable for a wide range of data features and complex scenarios. Each technique is designed to be adjusted to particular selection intents: the selection of consecutive dense regions, the selection of filament-like structures, and the selection of clusters—with all of them facilitating post-selection threshold adjustment. These techniques allow users to precisely select those regions of space for further exploration—with simple and approximate 3D pointing, brushing, or drawing input—using flexible point- or path-based input and without being limited by 3D occlusions, non-homogeneous feature density, or complex data shapes. These new techniques are evaluated in a controlled experiment and compared with the Baseline method, a region-based 3D painting selection. Our results indicate that our techniques are effective in handling a wide range of scenarios and allow users to select data based on their comprehension of crucial features. Furthermore, we analyze the attributes, requirements, and strategies of our spatial selection methods and compare them with existing state-of-the-art selection methods to handle diverse data features and situations. Based on this analysis we provide guidelines for choosing the most suitable 3D spatial selection techniques based on the interaction environment, the given data characteristics, or the need for interactive post-selection threshold adjustment.
Metrics-Based Evaluation and Comparison of Visualization Notations
Authors: Nicolas Kruchten, Andrew M. McNutt, Michael J. McGuffin
Published at Vis, 2023

A visualization notation is a recurring pattern of symbols used to author specifications of visualizations, from data transformation to visual mapping. Programmatic notations use symbols defined by grammars or domain-specific languages (e.g. ggplot2, dplyr, Vega-Lite) or libraries (e.g. Matplotlib, Pandas). Designers and prospective users of grammars and libraries often evaluate visualization notations by inspecting galleries of examples. While such collections demonstrate usage and expressiveness, their construction and evaluation are usually ad hoc, making comparisons of different notations difficult. More rarely, experts analyze notations via usability heuristics, such as the Cognitive Dimensions of Notations framework. These analyses, akin to structured close readings of text, can reveal design deficiencies, but place a burden on the expert to simultaneously consider many facets of often complex systems. To alleviate these issues, we introduce a metrics-based approach to usability evaluation and comparison of notations in which metrics are computed for a gallery of examples across a suite of notations. While applicable to any visualization domain, we explore the utility of our approach via a case study considering statistical graphics that explores 40 visualizations across 9 widely used notations. We facilitate the computation of appropriate metrics and analysis via a new tool called NotaScope. We gathered feedback via interviews with authors or maintainers of prominent charting libraries ($n=6$). We find that this approach is a promising way to formalize, externalize, and extend evaluations and comparisons of visualization notations.
MolSieve: A Progressive Visual Analytics System for Molecular Dynamics Simulations
Authors: Rostyslav Hnatyshyn, Jieqiong Zhao, Danny Perez, James P. Ahrens, Ross Maciejewski
Published at Vis, 2023

Molecular Dynamics (MD) simulations are ubiquitous in cutting-edge physio-chemical research. They provide critical insights into how a physical system evolves over time given a model of interatomic interactions. Understanding a system's evolution is key to selecting the best candidates for new drugs, materials for manufacturing, and countless other practical applications. With today's technology, these simulations can encompass millions of unit transitions between discrete molecular structures, spanning up to several milliseconds of real time. Attempting to perform a brute-force analysis with data-sets of this size is not only computationally impractical, but would not shed light on the physically-relevant features of the data. Moreover, there is a need to analyze simulation ensembles in order to compare similar processes in differing environments. These problems call for an approach that is analytically transparent, computationally efficient, and flexible enough to handle the variety found in materials-based research. In order to address these problems, we introduce MolSieve, a progressive visual analytics system that enables the comparison of multiple long-duration simulations. Using MolSieve, analysts are able to quickly identify and compare regions of interest within immense simulations through its combination of control charts, data-reduction techniques, and highly informative visual components. A simple programming interface is provided which allows experts to fit MolSieve to their needs. To demonstrate the efficacy of our approach, we present two case studies of MolSieve and report on findings from domain collaborators.
Mosaic: An Architecture for Scalable & Interoperable Data Views
Authors: Jeffrey Heer, Dominik Moritz
Published at Vis, 2023

Mosaic is an architecture for greater scalability, extensibility, and interoperability of interactive data views. Mosaic decouples data processing from specification logic: clients publish their data needs as declarative queries that are then managed and automatically optimized by a coordinator that proxies access to a scalable data store. Mosaic generalizes Vegalite's selection abstraction to enable rich integration and linking across visualizations and components such as menus, text search, and tables. We demonstrate Mosaic's expressiveness, extensibility, and interoperability through examples that compose diverse visualization, interaction, and optimization techniques—many constructed using vgplot, a grammar of interactive graphics in which graphical marks act as Mosaic clients. To evaluate scalability, we present benchmark studies with order-of-magnitude performance improvements over existing web-based visualization systems—enabling flexible, real-time visual exploration of billion+ record datasets. We conclude by discussing Mosaic's potential as an open platform that bridges visualization languages, scalable visualization, and interactive data systems more broadly.
My Model is Unfair, Do People Even Care? Visual Design Affects Trust and Perceived Bias in Machine Learning
Authors: Aimen Gaba, Zhanna Kaufman, Jason Cheung, Marie Shvakel, Kyle Wm. Hall, Yuriy Brun, Cindy Xiong Bearfield
Published at Vis, 2023

Machine learning technology has become ubiquitous, but, unfortunately, often exhibits bias. As a consequence, disparate stakeholders need to interact with and make informed decisions about using machine learning models in everyday systems. Visualization technology can support stakeholders in understanding and evaluating trade-offs between, for example, accuracy and fairness of models. This paper aims to empirically answer “Can visualization design choices affect a stakeholder's perception of model bias, trust in a model, and willingness to adopt a model?” Through a series of controlled, crowd-sourced experiments with more than 1,500 participants, we identify a set of strategies people follow in deciding which models to trust. Our results show that men and women prioritize fairness and performance differently and that visual design choices significantly affect that prioritization. For example, women trust fairer models more often than men do, participants value fairness more when it is explained using text than as a bar chart, and being explicitly told a model is biased has a bigger impact than showing past biased performance. We test the generalizability of our results by comparing the effect of multiple textual and visual design choices and offer potential explanations of the cognitive mechanisms behind the difference in fairness perception and trust. Our research guides design considerations to support future work developing visualization systems for machine learning.