arXiv subjects

Remco Chang

Publications and source records attributed to Remco Chang.

At least 19 recordsLinked to original sources

arXiv2026

A Data-Centric Perspective on Tree Visualizations

Tree visualization (TreeVis) techniques span diverse designs. Existing taxonomies organize them by visual characteristics such as layout dimensionality, edge representation, and node alignment. However, this visual-centric perspective can obscure structural similarities and make it difficult to determine whether differences arise from data structures or visual encodings. We investigate TreeVis techniques from a data-centric perspective grounded in Prepared Tables, the final data state prior to visual encoding. Using TreeVis.net, we curate 133 two-dimensional techniques and characterize each by the object records and attribute roles required before encoding. Our analysis shows that the corpus is more concentrated at the prepared-data level than a visual reading would suggest. The techniques collapse to a small set of recurring object combinations and schemas. Many techniques across TreeVis representation categories share the same schema, suggesting that much of the apparent diversity of TreeVis designs lies in visual representation rather than fundamentally different pre-encoding data requirements. Prepared Table schemas therefore support reasoning about structural equivalence, sufficiency, and difference across TreeVis designs.

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arXiv2026

ZipLine: Visual Analysis of Multivariate Graphs with Predicate Logic

Multivariate graphs unite two distinct data perspectives: a topological structure defined by nodes and edges, and attribute data associated with each node. Analyzing such graphs therefore requires reasoning across two complementary spaces. However, existing systems typically emphasize the analysis of one space at a time, focusing either on topology or on attributes. As a result, exploration, analysis, and pattern discovery that depend on their interaction remain difficult. In this paper, we present ZipLine, a system designed to support integrative analysis of multivariate graphs by bridging both topology and attribute spaces. ZipLine introduces a predicate language that enables analysts to express patterns involving topology, node attributes, and neighborhood relations with a unified formalism. The system further provides a predicate-learning algorithm that maps analyst interactions across both topology (e.g., subgraph selection) and attribute views (e.g., value brushing), into the predicate language, enabling learned expressions that bridge the two spaces. This integrative approach supports iterative analysis by enabling analysts to refine patterns through coordinated reasoning over topology and attributes. We demonstrate ZipLine through three case studies in energy infrastructure, cybersecurity, and drug discovery analysis. The results show that ZipLine enables expressive multivariate graph analysis through unified reasoning across topology and attributes.

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arXiv2026

GraphPolaris: A System for Query, Analysis, and Visualization of Graph Databases

Graph databases are increasingly adopted as alternatives to tabular, aggregation-focused data models used in business intelligence (BI) systems such as Tableau, Power BI, and Looker. They capture complex relationships between entities, processes, and events, enabling analysis of information propagation in networks. As a result, graph analysis is central to applications such as fraud detection, social influence analysis, and supply chain resilience. Despite these advantages, existing tools do not adequately support interactive analysis of graph databases. Tabular BI systems lack mechanisms for reasoning over nodes and edges, while graph databases require specialized query languages and fragmented workflows that hinder accessibility. We present GraphPolaris, a no-code Visual Analytics system that enables users to explore, analyze, and visualize graph databases without programming skills. At its core, GraphPolaris features the GRAPHPOLARIS QUERY LANGUAGE (GPQL), a formal query grammar that facilitates flexible and composable graph queries, providing a formal foundation for analyzing relationships and graph patterns. GPQL serves as an intermediary between user interactions and the underlying database. Its formal foundation enables no-code query construction, database-agnostic query generation, and guarantees that every interaction produces a valid executable query. Informed by a formative user study, we designed GraphPolaris' interface and visualizations to lower technical barriers and foster iterative, collaborative exploration of complex networks. We evaluate GraphPolaris through two real-world case studies in telecommunications and supply-chain analysis and a 22-month-long formative mixed-method study, including a MILC-based assessment of its fit to analysts' graph analytics workflows.

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arXiv2026

LatentGandr: Visual Exploration of Generative AI Latent Space via Local Embeddings

Generative AI has demonstrated significant potential in creative design, enabling the rapid generation of visual content and imaginative concepts. Although deep AI models achieve effective featurization in the latent space, navigating the space remains a challenge. Current techniques, such as GANSlider and SliderSpace, use multiple sliders to generate high-dimensional vectors in generative AI's latent space. Despite applying (global) PCA to reduce the number of sliders, these approaches struggle with scalability and usability as the number of control dimensions increases. In this paper, we introduce LatentGandr, a visual analytics technique that facilitates latent space exploration by extracting locally linear dimensions from embeddings in high-dimensional latent spaces. By analyzing the topology and local curvature of the embeddings, LatentGandr automatically identifies local neighborhoods and computes their principal components using localized PCA. These local principal components are visualized as interactive image grids, allowing users to efficiently explore and control the generative process, providing an intuitive means to refine the generation of novel content and concepts. To evaluate the effectiveness of LatentGandr, we conducted a study comparing it to GANSlider, the current state-of-the-art visualization interface for generative AI models. The results offer insights into how localized exploration techniques can enhance user interaction with these models.

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arXiv2026

Visual Decoding Operators: Towards a Compositional Theory of Visualization Perception

Prior work on perceptual effectiveness has decomposed visualizations into smaller common units (e.g., channels such as angle, position, and length) to establish rankings. While useful, these decompositions lack the computational structure to predict performance for new visualization x task combinations, requiring new experiments for each. We propose an alternative unit of analysis: operationalizing quantitative visualization interpretation as sequences of composable visual decoding operators. Using probability density function (PDF) and cumulative distribution function (CDF) charts, we examine how four chart-specific tasks can be decomposed into five reusable, chart-agnostic perceptual operations and characterize their error profiles through hierarchical Bayesian modeling. We then test generalizability by composing one kind of learned operators to predict performance on a structurally different task: Moritz et al.'s [37] scatterplot mean-estimation experiment, where the chart type, chart dimensions, and analytic goal all differ from the learning conditions. With a pre-registered analysis plan, we compose operators under six candidate strategies and evaluate each against empirical data with no parameters fit to the response data. One strategy captures both bias and variance of observed responses; five alternatives fail in distinguishable ways. We argue that this decoding-operator-oriented approach to empirical visualization research demonstrates the feasibility of a different way of doing empirical visualization research, one where findings compose, and predictions extend beyond the conditions in which they were measured. Free copy of this paper and supplemental materials: https://osf.io/prtfq.

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arXiv2025

AlloyLens: A Visual Analytics Tool for High-throughput Alloy Screening and Inverse Design

Designing multi-functional alloys requires exploring high-dimensional composition-structure-property spaces, yet current tools are limited to low-dimensional projections and offer limited support for sensitivity or multi-objective tradeoff reasoning. We introduce AlloyLens, an interactive visual analytics system combining a coordinated scatterplot matrix (SPLOM), dynamic parameter sliders, gradient-based sensitivity curves, and nearest neighbor recommendations. This integrated approach reveals latent structure in simulation data, exposes the local impact of compositional changes, and highlights tradeoffs when exact matches are absent. We validate the system through case studies co-developed with domain experts spanning structural, thermal, and electrical alloy design.

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arXiv2024

Beware of Validation by Eye: Visual Validation of Linear Trends in Scatterplots

Visual validation of regression models in scatterplots is a common practice for assessing model quality, yet its efficacy remains unquantified. We conducted two empirical experiments to investigate individuals' ability to visually validate linear regression models (linear trends) and to examine the impact of common visualization designs on validation quality. The first experiment showed that the level of accuracy for visual estimation of slope (i.e., fitting a line to data) is higher than for visual validation of slope (i.e., accepting a shown line). Notably, we found bias toward slopes that are "too steep" in both cases. This lead to novel insights that participants naturally assessed regression with orthogonal distances between the points and the line (i.e., ODR regression) rather than the common vertical distances (OLS regression). In the second experiment, we investigated whether incorporating common designs for regression visualization (error lines, bounding boxes, and confidence intervals) would improve visual validation. Even though error lines reduced validation bias, results failed to show the desired improvements in accuracy for any design. Overall, our findings suggest caution in using visual model validation for linear trends in scatterplots.

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arXiv2024

Design-Specific Transformations in Visualization

In visualization, the process of transforming raw data into visually comprehensible representations is pivotal. While existing models like the Information Visualization Reference Model describe the data-to-visual mapping process, they often overlook a crucial intermediary step: design-specific transformations. This process, occurring after data transformation but before visual-data mapping, further derives data, such as groupings, layout, and statistics, that are essential to properly render the visualization. In this paper, we advocate for a deeper exploration of design-specific transformations, highlighting their importance in understanding visualization properties, particularly in relation to user tasks. We incorporate design-specific transformations into the Information Visualization Reference Model and propose a new formalism that encompasses the user task as a function over data. The resulting formalism offers three key benefits over existing visualization models: (1) describing task as compositions of functions, (2) enabling analysis of data transformations for visual-data mapping, and (3) empowering reasoning about visualization correctness and effectiveness. We further discuss the potential implications of this model on visualization theory and visualization experiment design.

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arXiv2024

A Typology of Decision-Making Tasks for Visualization

Despite decision-making being a vital goal of data visualization, little work has been done to differentiate decision-making tasks within the field. While visualization task taxonomies and typologies exist, they often focus on more granular analytical tasks that are too low-level to describe large complex decisions, which can make it difficult to reason about and design decision-support tools. In this paper, we contribute a typology of decision-making tasks that were iteratively refined from a list of design goals distilled from a literature review. Our typology is concise and consists of only three tasks: CHOOSE, ACTIVATE, and CREATE. Although decision types originating in other disciplines exist, we provide definitions for these tasks that are suitable for the visualization community. Our proposed typology offers two benefits. First, the ability to compose and hierarchically organize the tasks enables flexible and clear descriptions of decisions with varying levels of complexities. Second, the typology encourages productive discourse between visualization designers and domain experts by abstracting the intricacies of data, thereby promoting clarity and rigorous analysis of decision-making processes. We demonstrate the benefits of our typology through four case studies, and present an evaluation of the typology from semi-structured interviews with experienced members of the visualization community who have contributed to developing or publishing decision support systems for domain experts. Our interviewees used our typology to delineate the decision-making processes supported by their systems, demonstrating its descriptive capacity and effectiveness. Finally, we present preliminary findings on the usefulness of our typology for visualization design.

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arXiv2024

DimBridge: Interactive Explanation of Visual Patterns in Dimensionality Reductions with Predicate Logic

Dimensionality reduction techniques are widely used for visualizing high-dimensional data. However, support for interpreting patterns of dimension reduction results in the context of the original data space is often insufficient. Consequently, users may struggle to extract insights from the projections. In this paper, we introduce DimBridge, a visual analytics tool that allows users to interact with visual patterns in a projection and retrieve corresponding data patterns. DimBridge supports several interactions, allowing users to perform various analyses, from contrasting multiple clusters to explaining complex latent structures. Leveraging first-order predicate logic, DimBridge identifies subspaces in the original dimensions relevant to a queried pattern and provides an interface for users to visualize and interact with them. We demonstrate how DimBridge can help users overcome the challenges associated with interpreting visual patterns in projections.

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arXiv2024

Preliminary Guidelines For Combining Data Integration and Visual Data Analysis

Data integration is often performed to consolidate information from multiple disparate data sources during visual data analysis. However, integration operations are usually separate from visual analytics operations such as encode and filter in both interface design and empirical research. We conducted a preliminary user study to investigate whether and how data integration should be incorporated directly into the visual analytics process. We used two interface alternatives featuring contrasting approaches to the data preparation and analysis workflow: manual file-based ex-situ integration as a separate step from visual analytics operations; and automatic UI-based in-situ integration merged with visual analytics operations. Participants were asked to complete specific and free-form tasks with each interface, browsing for patterns, generating insights, and summarizing relationships between attributes distributed across multiple files. Analyzing participants' interactions and feedback, we found both task completion time and total interactions to be similar across interfaces and tasks, as well as unique integration strategies between interfaces and emergent behaviors related to satisficing and cognitive bias. Participants' time spent and interactions revealed that in-situ integration enabled users to spend more time on analysis tasks compared with ex-situ integration. Participants' integration strategies and analytical behaviors revealed differences in interface usage for generating and tracking hypotheses and insights. With these results, we synthesized preliminary guidelines for designing future visual analytics interfaces that can support integrating attributes throughout an active analysis process.

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arXiv2023

RekomGNN: Visualizing, Contextualizing and Evaluating Graph Neural Networks Recommendations

Content recommendation tasks increasingly use Graph Neural Networks, but it remains challenging for machine learning experts to assess the quality of their outputs. Visualization systems for GNNs that could support this interrogation are few. Moreover, those that do exist focus primarily on exposing GNN architectures for tuning and prediction tasks and do not address the challenges of recommendation tasks. We developed RekomGNN, a visual analytics system that supports ML experts in exploring GNN recommendations across several dimensions and making annotations about their quality. RekomGNN straddles the design space between Neural Network and recommender system visualization to arrive at a set of encoding and interaction choices for recommendation tasks. We found that RekomGNN helps experts make qualitative assessments of the GNN's results, which they can use for model refinement. Overall, our contributions and findings add to the growing understanding of visualizing GNNs for increasingly complex tasks.

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arXiv2023

Visual Validation versus Visual Estimation: A Study on the Average Value in Scatterplots

We investigate the ability of individuals to visually validate statistical models in terms of their fit to the data. While visual model estimation has been studied extensively, visual model validation remains under-investigated. It is unknown how well people are able to visually validate models, and how their performance compares to visual and computational estimation. As a starting point, we conducted a study across two populations (crowdsourced and volunteers). Participants had to both visually estimate (i.e, draw) and visually validate (i.e., accept or reject) the frequently studied model of averages. Across both populations, the level of accuracy of the models that were considered valid was lower than the accuracy of the estimated models. We find that participants' validation and estimation were unbiased. Moreover, their natural critical point between accepting and rejecting a given mean value is close to the boundary of its 95% confidence interval, indicating that the visually perceived confidence interval corresponds to a common statistical standard. Our work contributes to the understanding of visual model validation and opens new research opportunities.

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arXiv2023

A Grammar of Hypotheses for Visualization, Data, and Analysis

We present a grammar for expressing hypotheses in visual data analysis to formalize the previously abstract notion of "analysis tasks." Through the lens of our grammar, we lay the groundwork for how a user's data analysis questions can be operationalized and automated as a set of hypotheses (a hypothesis space). We demonstrate that our grammar-based approach for analysis tasks can provide a systematic method towards unifying three disparate spaces in visualization research: the hypotheses a dataset can express (a data hypothesis space), the hypotheses a user would like to refine or verify through analysis (an analysis hypothesis space), and the hypotheses a visualization design is capable of supporting (a visualization hypothesis space). We illustrate how the formalization of these three spaces can inform future research in visualization evaluation, knowledge elicitation, analytic provenance, and visualization recommendation by using a shared language for hypotheses. Finally, we compare our proposed grammar-based approach with existing visual analysis models and discuss the potential of a new hypothesis-driven theory of visual analytics.

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arXiv2023

Knowledge Graphs in Practice: Characterizing their Users, Challenges, and Visualization Opportunities

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.

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arXiv2023

A Problem Space for Designing Visualizations

Visualization researchers and visualization professionals seek appropriate abstractions of visualization requirements that permit considering visualization solutions independently from specific problems. Abstractions can help us design, analyze, organize, and evaluate the things we create. The literature has many task structures (taxonomies, typologies, etc.), design spaces, and related ``frameworks'' that provide abstractions of the problems a visualization is meant to address. In this viewpoint, we introduce a different one, a problem space that complements existing frameworks by focusing on the needs that a visualization is meant to solve. We believe it provides a valuable conceptual tool for designing and discussing visualizations.

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arXiv2022

Analysis Without Data: Teaching Students to Tackle the VAST Challenge

The VAST Challenges have been shown to be an effective tool in visual analytics education, encouraging student learning while enforcing good visualization design and development practices. However, research has observed that students often struggle at identifying a good "starting point" when tackling the VAST Challenge. Consequently, students who could not identify a good starting point failed at finding the correct solution to the challenge. In this paper, we propose a preliminary guideline for helping students approach the VAST Challenge and identify initial analysis directions. We recruited two students to analyze the VAST 2017 Challenge using a hypothesis-driven approach, where they were required to pre-register their hypotheses prior to inspecting and analyzing the full dataset. From their experience, we developed a prescriptive guideline for other students to tackle VAST Challenges. In a preliminary study, we found that the students were able to use the guideline to generate well-formed hypotheses that could lead them towards solving the challenge. Additionally, the students reported that with the guideline, they felt like they had concrete steps that they could follow, thereby alleviating the burden of identifying a good starting point in their analysis process.

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arXiv2022

PIXAL: Anomaly Reasoning with Visual Analytics

Anomaly detection remains an open challenge in many application areas. While there are a number of available machine learning algorithms for detecting anomalies, analysts are frequently asked to take additional steps in reasoning about the root cause of the anomalies and form actionable hypotheses that can be communicated to business stakeholders. Without the appropriate tools, this reasoning process is time-consuming, tedious, and potentially error-prone. In this paper we present PIXAL, a visual analytics system developed following an iterative design process with professional analysts responsible for anomaly detection. PIXAL is designed to fill gaps in existing tools commonly used by analysts to reason with and make sense of anomalies. PIXAL consists of three components: (1) an algorithm that finds patterns by aggregating multiple anomalous data points using first-order predicates, (2) a visualization tool that allows the analyst to build trust in the algorithmically-generated predicates by performing comparative and counterfactual analyses, and (3) a visualization tool that helps the analyst generate and validate hypotheses by exploring which features in the data most explain the anomalies. Finally, we present the results of a qualitative observational study with professional analysts. These results of the study indicate that PIXAL facilitates the anomaly reasoning process, allowing analysts to make sense of anomalies and generate hypotheses that are meaningful and actionable to business stakeholders.

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