Detecting and interpreting common patterns in relational data is crucial for understanding complex topological structures across various domains. These patterns, or network motifs, can often be detected algorithmically. However, visual inspection remains vital for exploring and discovering patterns. This paper focuses on presenting motifs within BioFabric network visualizations-a unique technique that opens opportunities for research on scaling to larger networks, design variations, and layout algorithms to better expose motifs. Our goal is to show how highlighting motifs can assist users in identifying and interpreting patterns in BioFabric visualizations. To this end, we leverage existing motif simplification techniques. We replace edges with glyphs representing fundamental motifs such as staircases, cliques, paths, and connector nodes. The results of our controlled experiment and usage scenarios demonstrate that motif simplification for BioFabric is useful for detecting and interpreting network patterns. Our participants were faster and more confident using the simplified view without sacrificing accuracy. The efficacy of our current motif simplification approach depends on which extant layout algorithm is used. We hope our promising findings on user performance will motivate future research on layout algorithms tailored to maximizing motif presentation. Our supplemental material is available at https://osf.io/f8s3g/?view_only=7e2df9109dfd4e6c85b89ed828320843.

As Differential Privacy (DP) transitions from theory to practice, visualization has surfaced as a catalyst in promoting acceptance and usage. Despite the potential of visualization tools to support differential privacy implementation, their development is limited by a lack of understanding of the overall deployment process, practitioner challenges, and the role of visual tools in real-world deployments. To narrow this gap, we interviewed 18 professionals from various backgrounds who regularly engage with differential privacy in their work. Our objectives were to understand the differential privacy implementation process and associated challenges; explore the actors (individuals involved in differential privacy implementation), how they use or struggle to use visualization; and identify the benefits and challenges of using visualization in the implementation process. Our results delineate the differential privacy implementation process into five distinct stages and highlight the main actors alongside the diverse visualization applications and shortcomings. We find that visualizations can be used to build foundational differential privacy knowledge, describe implementation parameters, and evaluate private outputs. However, the visualization strategies described often fail to address the diverse technical backgrounds and varied privacy and accuracy concerns of users, hindering effective communication between the different actors involved in the implementation process. From our findings, we propose three research directions: visualizations for setting and evaluating noise addition, evaluation of uncertainty visualization related to trust in differential privacy, and research focused on pedagogical visualizations for complex data science topics.

Layered network visualizations assign each node to one of several parallel axes. They can convey sequence or flow data, hierarchies, or multiple data classes, but edge crossings and long edges often impair readability. Layout algorithms can reduce edge crossings and shorten edges using quick heuristics or optimal methods that prioritize human readability over computation speed. This work uses an optimization metaheuristic to provide the best of both worlds: high-quality layouts within a predetermined execution time. Our adaptation of the large neighborhood search (LNS) metaheuristic repeatedly selects fixed-sized subgraphs to lay out optimally. We conducted a computational evaluation using 450 synthetic networks to compare five ways of selecting candidate nodes, four ways of selecting their neighboring subgraph, and three criteria for determining subgraph size. LNS generally halved the number of crossings versus the barycentric heuristic while maintaining a reasonable runtime. Our best approach randomly selected candidate nodes, used degree centrality to pick cluster-like neighborhoods, and chose smaller neighborhoods that could be optimally laid out in 0.6 or 1.2 seconds (versus 6 seconds). In a case study visualizing 13 control flow graphs, most with over 1000 nodes, we show that our method can be employed to create visualizations with fewer crossings than Tabu Search, another metaheuristic, and vastly outperforms an ILP solver when runtime is bounded.

A layered graph is an important category of graph in which every node is assigned to a layer, and layers are drawn as parallel or radial lines. They are commonly used to display temporal data or hierarchical graphs. Previous research has demonstrated that minimizing edge crossings is the most important criterion to consider when looking to improve the readability of such graphs. While heuristic approaches exist for crossing minimization, we are interested in optimal approaches to the problem that prioritize human readability over computational scalability. We aim to improve the usefulness and applicability of such optimal methods by understanding and improving their scalability to larger graphs. This paper categorizes and evaluates the state-of-the-art linear programming formulations for exact crossing minimization and describes nine new and existing techniques that could plausibly accelerate the optimization algorithm. Through a computational evaluation, we explore each technique's effect on calculation time and how the techniques assist or inhibit one another, allowing researchers and practitioners to adapt them to the characteristics of their graphs. Our best-performing techniques yielded a median improvement of 2.5-17 × depending on the solver used, giving us the capability to create optimal layouts faster and for larger graphs. We provide an open-source implementation of our methodology in Python, where users can pick which combination of techniques to enable according to their use case. A free copy of this paper and all supplemental materials, datasets used, and source code are available at https://osf.io/5vq79.

Fused Deposition Modeling (FDM) is a low-cost method of 3D printing that involves stacking horizontal layers of plastic. FDM is used to produce tactile graphics and interfaces for people with visual impairments. Unfortunately, the print orientation can alter the structure and quality of braille and text. The difference between printing braille vertically and horizontally has been documented. However, we found no comprehensive study of these angles or the angles in between, nor any study providing a quantitative and qualitative user evaluation. We conducted two mixed-methods studies to evaluate the performance of braille printed at different angles. We measured reading time and subjective preference and performed a thematic analysis of participants’ responses. Our participants were faster using and preferred 75° and vertical braille over horizontal braille. These results provide makers with guidelines for creating models with readable 3D-printed braille.

Abstract Evaluations—encompassing computational evaluations, benchmarks and user studies—are essential tools for validating the performance and applicability of graph and network layout algorithms (also known as graph drawing). These evaluations not only offer significant insights into an algorithm's performance and capabilities, but also assist the reader in determining if the algorithm is suitable for a specific purpose, such as handling graphs with a high volume of nodes or dense graphs. Unfortunately, there is no standard approach for evaluating layout algorithms. Prior work holds a ‘Wild West’ of diverse benchmark datasets and data characteristics, as well as varied evaluation metrics and ways to report results. It is often difficult to compare layout algorithms without first implementing them and then running your own evaluation. In this systematic review, we delve into the myriad of methodologies employed to conduct evaluations—the utilized techniques, reported outcomes and the pros and cons of choosing one approach over another. Our examination extends beyond computational evaluations, encompassing user‐centric evaluations, thus presenting a comprehensive understanding of algorithm validation. This systematic review—and its accompanying website—guides readers through evaluation types, the types of results reported, and the available benchmark datasets and their data characteristics. Our objective is to provide a valuable resource for readers to understand and effectively apply various evaluation methods for graph layout algorithms. A free copy of this paper and all supplemental material is available at osf.io , and the categorized papers are accessible on our website at https://visdunneright.github.io/gd‐comp‐eval/ .

Comparing relational languages by their logical expressiveness is well understood. Less well understood is how to compare relational languages by their ability to represent relational query patterns. Indeed, what are query patterns other than "a certain way of writing a query"? And how can query patterns be defined across procedural and declarative languages, irrespective of their syntax? To the best of our knowledge, we provide the first semantic definition of relational query patterns by using a variant of structure-preserving mappings between the relational tables of queries. This formalism allows us to analyze the relative pattern expressiveness of relational language fragments and create a hierarchy of languages with equal logical expressiveness yet different pattern expressiveness. Notably, for the non-disjunctive language fragment, we show that relational calculus can express a larger class of patterns than the basic operators of relational algebra. Our language-independent definition of query patterns opens novel paths for assisting database users. For example, these patterns could be leveraged to create visual query representations that faithfully represent query patterns, speed up interpretation, and provide visual feedback during query editing. As a concrete example, we propose Relational Diagrams, a complete and sound diagrammatic representation of safe relational calculus that is provably (i) unambiguous, (ii) relationally complete, and (iii) able to represent all query patterns for unions of non-disjunctive queries. Among all diagrammatic representations for relational queries that we are aware of, ours is the only one with these three properties. Furthermore, our anonymously preregistered user study shows that Relational Diagrams allow users to recognize patterns meaningfully faster and more accurately than SQL.

Increasingly, visualization practitioners are working with, using, and studying private and sensitive data. There can be many stakeholders interested in the resulting analyses-but widespread sharing of the data can cause harm to individuals, companies, and organizations. Practitioners are increasingly turning to differential privacy to enable public data sharing with a guaranteed amount of privacy. Differential privacy algorithms do this by aggregating data statistics with noise, and this now-private data can be released visually with differentially private scatterplots. While the private visual output is affected by the algorithm choice, privacy level, bin number, data distribution, and user task, there is little guidance on how to choose and balance the effect of these parameters. To address this gap, we had experts examine 1,200 differentially private scatterplots created with a variety of parameter choices and tested their ability to see aggregate patterns in the private output (i.e. the visual utility of the chart). We synthesized these results to provide easy-to-use guidance for visualization practitioners releasing private data through scatterplots. Our findings also provide a ground truth for visual utility, which we use to benchmark automated utility metrics from various fields. We demonstrate how multi-scale structural similarity (MS-SSIM), the metric most strongly correlated with our study's utility results, can be used to optimize parameter selection.

Immersive analytics has emerged as a promising research area, leveraging advances in immersive display technologies and techniques, such as virtual and augmented reality, to facilitate data exploration and decision-making. This paper presents a systematic literature review of 73 studies published between 2013-2022 on immersive analytics systems and visualizations, aiming to identify and categorize the primary dimensions influencing their design. We identified five key dimensions: Academic Theory and Contribution, Immersive Technology, Data, Spatial Presentation, and Visual Presentation. Academic Theory and Contribution assess the motivations behind the works and their theoretical frameworks. Immersive Technology examines the display and input modalities, while Data dimension focuses on dataset types and generation. Spatial Presentation discusses the environment, space, embodiment, and collaboration aspects in IA, and Visual Presentation explores the visual elements, facet and position, and manipulation of views. By examining each dimension individually and cross-referencing them, this review uncovers trends and relationships that help inform the design of immersive systems visualizations. This analysis provides valuable insights for researchers and practitioners, offering guidance in designing future immersive analytics systems and shaping the trajectory of this rapidly evolving field.

Many collaborative data analysis situations benefit from collaborators utilizing different platforms. However, maintaining group awareness between team members using diverging devices is difficult, not least because common ground diminishes. A person using head-mounted VR cannot physically see a user on a desktop computer even while co-located, and the desktop user cannot easily relate to the VR user’s 3D workspace. To address this, we propose the “eyes-and-shoes” principles for group awareness and abstract them into four levels of techniques. Furthermore, we evaluate these principles with a qualitative user study of 6 participant pairs synchronously collaborating across distributed desktop and VR head-mounted devices. In this study, we vary the group awareness techniques between participants and explore two visualization contexts within participants. The results of this study indicate that the more visual metaphors and views of participants diverge, the greater the level of group awareness is needed. A copy of this paper, the study preregistration, and all supplemental materials required to reproduce the study are available on OSF (link).

Abstract Design studies are an integral method of visualization research with hundreds of instances in the literature. Although taught as a theory, the practical implementation of design studies is often excluded from visualization pedagogy due to the lengthy time commitments associated with such studies. Recent research has addressed this challenge and developed an expedited design study framework, the Design Study “Lite” Methodology (DSLM), which can implement design studies with novice students within just 14 weeks. The framework was developed and evaluated based on five semesters of in‐person data visualization courses with 30 students or less and was implemented in conjunction with Service‐Learning (S‐L). With the growth and popularity of the data visualization field—and the teaching environment created by the COVID‐19 pandemic—more academic institutions are offering visualization courses online. Therefore, in this paper, we strengthen and validate the epistemological foundations of the DSLM framework by testing its (1) adaptability to online learning environments and conditions and (2) scalability to larger classes with up to 57 students. We present two online implementations of the DSLM framework, with and without Service‐Learning (S‐L), to test the adaptability and scalability of the framework. We further demonstrate that the framework can be applied effectively without the S‐L component. We reflect on our experience with the online DSLM implementations and contribute a detailed retrospective analysis using thematic analysis and grounded theory methods to draw valuable recommendations and guidelines for future applications of the framework. This work verifies that DSLM can be used successfully in online classes to teach design study methodology. Finally, we contribute novel additions to the DSLM framework to further enhance it for teaching and learning design studies in the classroom. The preprint and supplementary materials for this paper can be found at https://osf.io/6bjx5/.

Internet routing is largely dependent on Border Gateway Protocol (BGP). However, BGP does not have any inherent authentication or integrity mechanisms that help make it secure. Effective security is challenging or infeasible to implement due to high costs, policy employment in these distributed systems, and unique routing behavior. Visualization tools provide an attractive alternative in lieu of traditional security approaches. Several BGP security visualization tools have been developed as a stop-gap in the face of ever-present BGP attacks. Even though the target users, tasks, and domain remain largely consistent across such tools, many diverse visualization designs have been proposed. The purpose of this study is to provide an initial formalization of methods and visualization techniques for BGP cybersecurity analysis. Using PRISMA guidelines, we provide a systematic review and survey of 29 BGP visualization tools with their tasks, implementation techniques, and attacks and anomalies that they were intended for. We focused on BGP visualization tools as the main inclusion criteria to best capture the visualization techniques used in this domain while excluding solely algorithmic solutions and other detection tools that do not involve user interaction or interpretation. We take the unique approach of connecting (1) the actual BGP attacks and anomalies used to validate existing tools with (2) the techniques employed to detect them. In this way, we contribute an analysis of which techniques can be used for each attack type. Furthermore, we can see the evolution of visualization solutions in this domain as new attack types are discovered. This systematic review provides the groundwork for future designers and researchers building visualization tools for providing BGP cybersecurity, including an understanding of the state-of-the-art in this space and an analysis of what techniques are appropriate for each attack type. Our novel security visualization survey methodology-connecting visualization techniques with appropriate attack types-may also assist future researchers conducting systematic reviews of security visualizations. All supplemental materials are available at https://osf.io/tupz6/.

Data visualization is pervasive in the lives of children as they encounter graphs and charts in early education and online media. In spite of this prevalence, our guidelines and understanding of how children perceive graphs stem primarily from studies conducted with adults. Previous psychology and education research indicates that children’s cognitive abilities are different from adults. Therefore, we conducted a classic graphical perception study on a population of children aged 8–12 enrolled in the Ivy After School Program in Boston, MA and adult computer science students enrolled in Northeastern University to determine how accurately participants judge differences in particular graphical encodings. We record the accuracy of participants’ answers for five encodings most commonly used with quantitative data. The results of our controlled experiment show that children have remarkably similar graphical perception to adults, but are consistently less accurate at interpreting the visual encodings. We found similar effectiveness rankings, relative differences in error between the different encodings, and patterns of bias across encoding types. Based on our findings, we provide design guidelines and recommendations for creating visualizations for children. This paper and all supplemental materials are available at https://osf.io/ygrdv.

Abstract Hypergraphs are a generalization of graphs in which edges (hyperedges) can connect more than two vertices—as opposed to ordinary graphs where edges involve only two vertices. Hypergraphs are a fairly common data structure but there is little consensus on how to visualize them. To optimize a hypergraph drawing for readability, we need a layout algorithm. Common graph layout algorithms only consider ordinary graphs and do not take hyperedges into account. We focus on layered hypergraphs, a particular class of hypergraphs that, like layered graphs, assigns every vertex to a layer, and the vertices in a layer are drawn aligned on a linear axis with the axes arranged in parallel. In this paper, we propose a general method to apply layered graph layout algorithms to layered hypergraphs. We introduce six different transformations for layered hypergraphs. The choice of transformation affects the subsequent graph layout algorithm in terms of computational performance and readability of the results. Thus, we perform a comparative evaluation of these transformations in terms of number of crossings, edge length, and impact on performance. We also provide two case studies showing how our transformations can be applied to real‐life use cases. A copy of this paper with all appendices and supplemental material is available at osf.io/grvwu.

Abstract Likert scales are often used in visualization evaluations to produce quantitative estimates of subjective attributes, such as ease of use or aesthetic appeal. However, the methods used to collect, analyze, and visualize data collected with Likert scales are inconsistent among evaluations in visualization papers. In this paper, we examine the use of Likert scales as a tool for measuring subjective response in a systematic review of 134 visualization evaluations published between 2009 and 2019. We find that papers with both objective and subjective measures do not hold the same reporting and analysis standards for both aspects of their evaluation, producing less rigorous work for the subjective qualities measured by Likert scales. Additionally, we demonstrate that many papers are inconsistent in their interpretations of Likert data as discrete or continuous and may even sacrifice statistical power by applying nonparametric tests unnecessarily. Finally, we identify instances where key details about Likert item construction with the potential to bias participant responses are omitted from evaluation methodology reporting, inhibiting the feasibility and reliability of future replication studies. We summarize recommendations from other fields for best practices with Likert data in visualization evaluations, based on the results of our survey. A full copy of this paper and all supplementary material are available at https://osf.io/exbz8/ .

Node-link visualizations are a familiar and powerful tool for displaying the relationships in a network. The readability of these visualizations highly depends on the spatial layout used for the nodes. In this paper, we focus on computing layered layouts, in which nodes are aligned on a set of parallel axes to better expose hierarchical or sequential relationships. Heuristic-based layouts are widely used as they scale well to larger networks and usually create readable, albeit sub-optimal, visualizations. We instead use a layout optimization model that prioritizes optimality - as compared to scalability - because an optimal solution not only represents the best attainable result, but can also serve as a baseline to evaluate the effectiveness of layout heuristics. We take an important step towards powerful and flexible network visualization by proposing Stratisfimal Layout, a modular integer-linear-programming formulation that can consider several important readability criteria simultaneously - crossing reduction, edge bendiness, and nested and multi-layer groups. The layout can be adapted to diverse use cases through its modularity. Individual features can be enabled and customized depending on the application. We provide open-source and documented implementations of the layout, both for web-based and desktop visualizations. As a proof-of-concept, we apply it to the problem of visualizing complicated SQL queries, which have features that we believe cannot be addressed by existing layout optimization models. We also include a benchmark network generator and the results of an empirical evaluation to assess the performance trade-offs of our design choices. A full version of this paper with all appendices, data, and source code is available at osf.io/qdyt9 with live examples at https://visdunneright.github.io/stratisfimal/.

The dominant markup language for Web visualizations-Scalable Vector Graphics (SVG)-is comparatively easy to learn, and is open, accessible, customizable via CSS, and searchable via the DOM, with easy interaction handling and debugging. Because these attributes allow visualization creators to focus on design on implementation details, tools built on top of SVG, such as D3.js, are essential to the visualization community. However, slow SVG rendering can limit designs by effectively capping the number of on-screen data points, and this can force visualization creators to switch to Canvas or WebGL. These are less flexible (e.g., no search or styling via CSS), and harder to learn. We introduce Scalable Scalable Vector Graphics (SSVG) to reduce these limitations and allow complex and smooth visualizations to be created with SVG. SSVG automatically translates interactive SVG visualizations into a dynamic virtual DOM (VDOM) to bypass the browser's slow 'to specification' rendering by intercepting JavaScript function calls. De-coupling the SVG visualization specification from SVG rendering, and obtaining a dynamic VDOM, creates flexibility and opportunity for visualization system research. SSVG uses this flexibility to free up the main thread for more interactivity and renders the visualization with Canvas or WebGL on a web worker. Together, these concepts create a drop-in JavaScript library which can improve rendering performance by 3-9× with only one line of code added. To demonstrate applicability, we describe the use of SSVG on multiple example visualizations including published visualization research. A free copy of this article, collected data, and source code are available as open science at osf.io/ge8wp.

Immersive Analytics is a quickly evolving field that unites several areas such as visualisation, immersive environments, and human-computer interaction to support human data analysis with emerging technologies. This research has thrived over the past years with multiple workshops, seminars, and a growing body of publications, spanning several conferences. Given the rapid advancement of interaction technologies and novel application domains, this paper aims toward a broader research agenda to enable widespread adoption. We present 17 key research challenges developed over multiple sessions by a diverse group of 24 international experts, initiated from a virtual scientific workshop at ACM CHI 2020. These challenges aim to coordinate future work by providing a systematic roadmap of current directions and impending hurdles to facilitate productive and effective applications for Immersive Analytics.

Virtual reality (VR) researchers struggle to conduct remote studies. Previous work has focused on working around limitations imposed by traditional crowdsourcing methods. However, the potential for leveraging social VR platforms for HCI evaluations is largely unexplored. These platforms have large VR-ready user populations, distributed synchronous virtual environments, and support for user-generated content. We demonstrate how social VR platforms can be used to practically and ethically produce valid research results by replicating two studies using one such platform (VRChat): a quantitative study on Fitts’ Law and a qualitative study on tabletop collaboration. Our replication studies exhibited analogous results to the originals, indicating the research validity of this approach. Moreover, we easily recruited experienced VR users with their own hardware for synchronous, remote, and collaborative participation. We further provide lessons learned for future researchers experimenting using social VR platforms. This paper and all supplemental materials are available at osf.io/c2amz.

Abstract Graphics Processing Units (GPUs) have been widely used to accelerate artificial intelligence, physics simulation, medical imaging, and information visualization applications. To improve GPU performance, GPU hardware designers need to identify performance issues by inspecting a huge amount of simulator‐generated traces. Visualizing the execution traces can reduce the cognitive burden of users and facilitate making sense of behaviors of GPU hardware components. In this paper, we first formalize the process of GPU performance analysis and characterize the design requirements of visualizing execution traces based on a survey study and interviews with GPU hardware designers. We contribute data and task abstraction for GPU performance analysis. Based on our task analysis, we propose Daisen, a framework that supports data collection from GPU simulators and provides visualization of the simulator‐generated GPU execution traces. Daisen features a data abstraction and trace format that can record simulator‐generated GPU execution traces. Daisen also includes a web‐based visualization tool that helps GPU hardware designers examine GPU execution traces, identify performance bottlenecks, and verify performance improvement. Our qualitative evaluation with GPU hardware designers demonstrates that the design of Daisen reflects the typical workflow of GPU hardware designers. Using Daisen, participants were able to effectively identify potential performance bottlenecks and opportunities for performance improvement. The open‐sourced implementation of Daisen can be found at gitlab.com/akita/vis . Supplemental materials including a demo video, survey questions, evaluation study guide, and post‐study evaluation survey are available at osf.io/j5ghq .

Temporal event sequence alignment has been used in many domains to visualize nuanced changes and interactions over time. Existing approaches align one or two sentinel events. Overview tasks require examining all alignments of interest using interaction and time or juxtaposition of many visualizations. Furthermore, any event attribute overviews are not closely tied to sequence visualizations. We present Sequence Braiding, a novel overview visualization for temporal event sequences and attributes using a layered directed acyclic network. Sequence Braiding visually aligns many temporal events and attribute groups simultaneously and supports arbitrary ordering, absence, and duplication of events. In a controlled experiment we compare Sequence Braiding and IDMVis on user task completion time, correctness, error, and confidence. Our results provide good evidence that users of Sequence Braiding can understand high-level patterns and trends faster and with similar error. A full version of this paper with all appendices; the evaluation stimuli, data, and analysis code; and source code are available at [Formula: see text].

Tools and interfaces are increasingly expected to be synchronous and distributed to accommodate remote collaboration. Yet, adoption of these techniques for data visualization is low partly because development is difficult: existing collaboration software systems either do not support simultaneous interaction or require expensive redevelopment of existing visualizations. We contribute VisConnect: a web-based synchronous distributed collaborative visualization system that supports most web-based SVG data visualizations, balances system safety with responsiveness, and supports simultaneous interaction from many collaborators. VisConnect works with existing visualization implementations with little-to-no code changes by synchronizing low-level JavaScript events across clients such that visualization updates proceed transparently across clients. This is accomplished via a peer-to-peer system that establishes consensus among clients on the per-element sequence of events, and uses a lock service to grant access over elements to clients. We contribute collaborative extensions of traditional visualization interaction techniques, such as drag, brush, and lasso, and discuss different strategies for collaborative visualization interactions. To demonstrate the utility of VisConnect, we present novel examples of collaborative visualizations in the healthcare domain, remote collaboration with annotation, and show in an education case study for e-learning with 22 participants that students found the ability to remotely collaborate on class activities helpful and enjoyable for understanding concepts. A free copy of this paper and source code are available on OSF at osf.io/ut7e6 and at visconnect.us.

Timelines are commonly represented on a horizontal line, which is not necessarily the most effective way to visualize temporal event sequences. However, few experiments have evaluated how timeline shape influences task performance. We present the design and results of a controlled experiment run on Amazon Mechanical Turk (n=192) in which we evaluate how timeline shape affects task completion time, correctness, and user preference. We tested 12 combinations of 4 shapes --- horizontal line, vertical line, circle, and spiral — and 3 data types — recurrent, non-recurrent, and mixed event sequences. We found good evidence that timeline shape meaningfully affects user task completion time but not correctness and that users have a strong shape preference. Building on our results, we present design guidelines for creating effective timeline visualizations based on user task and data types. A free copy of this paper, the evaluation stimuli and data, and code are available https://osf.io/qr5yu/

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 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 minimal in that no visual element is superfluous; they are unambiguous in that no two queries with different semantics map to the same visualization; and they 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 pattern-based and thus more intuitive use and re-use of SQL. A full version of this paper with all appendices and supplemental material for the experimental study (stimuli, raw data, and analysis code) are available at https://osf.io/btszh.

Abstract Autonomous unmanned aerial vehicles are complex systems of hardware, software, and human input. Understanding this complexity is key to their development and operation. Information visualizations already exist for exploring flight logs but comprehensive analyses currently require several disparate and custom tools. This design study helps address the pain points faced by autonomous unmanned aerial vehicle developers and operators. We contribute: a spiral development process model for grounded evaluation visualization development focused on progressively broadening target user involvement and refining user goals; a demonstration of the model as part of developing a deployed and adopted visualization system; a data and task abstraction for developers and operators performing post‐flight analysis of autonomous unmanned aerial vehicle logs; the design and implementation of D ata C omets , an open‐source and web‐based interactive visualization tool for post‐flight log analysis incorporating temporal, geospatial, and multivariate data; and the results of a summative evaluation of the visualization system and our abstractions based on in‐the‐wild usage. A free copy of this paper and source code are available at osf.io/h4p7g

Blood circulation in the human brain is supplied through a network of cerebral arteries. If a clinician suspects a patient has a stroke or other cerebrovascular condition, they order imaging tests. Neuroradiologists visually search the resulting scans for abnormalities. Their visual search tasks correspond to the abstract network analysis tasks of browsing and path following. To assist neuroradiologists in identifying cerebral artery abnormalities, we designed CerebroVis, a novel abstract-yet spatially contextualized-cerebral artery network visualization. In this design study, we contribute a novel framing and definition of the cerebral artery system in terms of network theory and characterize neuroradiologist domain goals as abstract visualization and network analysis tasks. Through an iterative, user-centered design process we developed an abstract network layout technique which incorporates cerebral artery spatial context. The abstract visualization enables increased domain task performance over 3D geometry representations, while including spatial context helps preserve the user's mental map of the underlying geometry. We provide open source implementations of our network layout technique and prototype cerebral artery visualization tool. We demonstrate the robustness of our technique by successfully laying out 61 open source brain scans. We evaluate the effectiveness of our layout through a mixed methods study with three neuroradiologists. In a formative controlled experiment our study participants used CerebroVis and a conventional 3D visualization to examine real cerebral artery imaging data to identify a simulated intracranial artery stenosis. Participants were more accurate at identifying stenoses using CerebroVis (absolute risk difference 13%). A free copy of this paper, the evaluation stimuli and data, and source code are available at osf.io/e5sxt.

Type 1 diabetes is a chronic, incurable autoimmune disease affecting millions of Americans in which the body stops producing insulin and blood glucose levels rise. The goal of intensive diabetes management is to lower average blood glucose through frequent adjustments to insulin protocol, diet, and behavior. Manual logs and medical device data are collected by patients, but these multiple sources are presented in disparate visualization designs to the clinician-making temporal inference difficult. We conducted a design study over 18 months with clinicians performing intensive diabetes management. We present a data abstraction and novel hierarchical task abstraction for this domain. We also contribute IDMVis: a visualization tool for temporal event sequences with multidimensional, interrelated data. IDMVis includes a novel technique for folding and aligning records by dual sentinel events and scaling the intermediate timeline. We validate our design decisions based on our domain abstractions, best practices, and through a qualitative evaluation with six clinicians. The results of this study indicate that IDMVis accurately reflects the workflow of clinicians. Using IDMVis, clinicians are able to identify issues of data quality such as missing or conflicting data, reconstruct patient records when data is missing, differentiate between days with different patterns, and promote educational interventions after identifying discrepancies.

Abstract An important part of network analysis is understanding community structures like topological clusters and attribute‐based groups. Standard approaches for showing communities using colour, shape, rectangular bounding boxes, convex hulls or force‐directed layout algorithms remain valuable, however our Group‐in‐a‐Box meta‐layouts add a fresh strategy for presenting community membership, internal structure and inter‐cluster relationships. This paper extends the basic Group‐in‐a‐Box meta‐layout, which uses a Treemap substrate of rectangular regions whose size is proportional to community size. When there are numerous inter‐community relationships, the proposed extensions help users view them more clearly: (1) the Croissant–Doughnut meta‐layout applies empirically determined rules for box arrangement to improve space utilization while still showing inter‐community relationships, and (2) the Force‐Directed layout arranges community boxes based on their aggregate ties at the cost of additional space. Our free and open source reference implementation in NodeXL includes heuristics to choose what we have found to be the preferable Group‐in‐a‐Box meta‐layout to show networks with varying numbers or sizes of communities. Case study examples, a pilot comparative user preference study (nine participants), and a readability measure‐based evaluation of 309 Twitter networks demonstrate the utility of the proposed meta‐layouts.

Social media, particularly Twitter, provides an abundance of real-time data. To account for this volume, researchers often use automated analysis and visualization techniques to produce a high-level overview of a Twitter stream. Existing techniques for understanding Twitter data make use of hashtags or word-pairs and may ignore the complex trends in discussions over time. To remedy this, we present an application of statistical topic modeling and alignment (binned topic models) to group related tweets into automatically generated topics and TopicFlow, an interactive tool to visualize the evolution of these topics. The effectiveness of this visualization for reasoning about large data sets is demonstrated by a usability study with 18 participants.

Analyzing networks involves understanding the complex relationships between entities, as well as any attributes they may have. The widely used node-link diagrams excel at this task, but many are difficult to extract meaning from because of the inherent complexity of the relationships and limited screen space. To help address this problem we introduce a technique called motif simplification, in which common patterns of nodes and links are replaced with compact and meaningful glyphs. Well-designed glyphs have several benefits: they (1) require less screen space and layout effort, (2) are easier to understand in the context of the network, (3) can reveal otherwise hidden relationships, and (4) preserve as much underlying information as possible. We tackle three frequently occurring and high-payoff motifs: fans of nodes with a single neighbor, connectors that link a set of anchor nodes, and cliques of completely connected nodes. We contribute design guidelines for motif glyphs; example glyphs for the fan, connector, and clique motifs; algorithms for detecting these motifs; a free and open source reference implementation; and results from a controlled study of 36 participants that demonstrates the effectiveness of motif simplification.

Exploring large network datasets, such as scientific collaboration networks, is challenging because they often contain a large number of nodes and edges in several types and with multiple attributes. Analyses of such networks are often long and complex, and may require several sessions by multiple users. Therefore, it is often difficult for users to recall their own exploration history or share it with others. We introduce GraphTrail, an interactive visualization for analyzing networks through exploration of node and edge aggregates that captures users' interactions and integrates this history directly in the exploration workspace. To facilitate large network analysis, GraphTrail integrates aggregation with familiar charts, drag-and-drop interaction on a canvas, and a novel pivoting mechanism for transitioning between aggregates. Through a three-month field study with a team of archeologists and a qualitative lab study with ten users, we demonstrate the effectiveness of our design and the benefits of integrated exploration history, including analysis comprehension, insight discovery, and exploration recall.

Keeping up with rapidly growing research fields, especially when there are multiple interdisciplinary sources, requires substantial effort for researchers, program managers, or venture capital investors. Current theories and tools are directed at finding a paper or website, not gaining an understanding of the key papers, authors, controversies, and hypotheses. This report presents an effort to integrate statistics, text analytics, and visualization in a multiple coordinated window environment that supports exploration. Our prototype system, Action Science Explorer (ASE), provides an environment for demonstrating principles of coordination and conducting iterative usability tests of them with interested and knowledgeable users. We developed an understanding of the value of reference management, statistics, citation text extraction, natural language summarization for single and multiple documents, filters to interactively select key papers, and network visualization to see citation patterns and identify clusters. A three‐phase usability study guided our revisions to ASE and led us to improve the testing methods.

Action Science Explorer (ASE) is a tool designed to support users in rapidly generating readily consumable summaries of academic literature. It uses citation network visualization, ranking and filtering papers by network statistics, and automatic clustering and summarization techniques. We describe how early formative evaluations of ASE led to a mature system evaluation, consisting of an in-depth empirical evaluation with four domain experts. The evaluation tasks were of two types: predefined tasks to test system performance in common scenarios, and user-defined tasks to test the system's usefulness for custom exploration goals. The primary contribution of this paper is a validation of the ASE design and recommendations to provide: easy-to-understand metrics for ranking and filtering documents, user control over which document sets to explore, and overviews of the document set in coordinated views along with details-on-demand of specific papers. We contribute a taxonomy of features for literature search and exploration tools and describe exploration goals identified by our participants.