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1.
Artigo em Inglês | MEDLINE | ID: mdl-36191101

RESUMO

This work investigates and compares the performance of node-link diagrams, adjacency matrices, and bipartite layouts for visualizing networks. In a crowd-sourced user study (n = 150), we measure the task accuracy and completion time of the three representations for different network classes and properties. In contrast to the literature, which covers mostly topology-based tasks (e.g., path finding) in small datasets, we mainly focus on overview tasks for large and directed networks. We consider three overview tasks on networks with 500 nodes: (T1) network class identification, (T2) cluster detection, and (T3) network density estimation, and two detailed tasks: (T4) node in-degree vs. out-degree and (T5) representation mapping, on networks with 50 and 20 nodes, respectively. Our results show that bipartite layouts are beneficial for revealing the overall network structure, while adjacency matrices are most reliable across the different tasks.

2.
Artigo em Inglês | MEDLINE | ID: mdl-36166539

RESUMO

A plethora of dimensionality reduction techniques have emerged over the past decades, leaving researchers and analysts with a wide variety of choices for reducing their data, all the more so given some techniques come with additional hyper-parametrization (e.g., t-SNE, UMAP, etc.). Recent studies are showing that people often use dimensionality reduction as a black-box regardless of the specific properties the method itself preserves. Hence, evaluating and comparing 2D embeddings is usually qualitatively decided, by setting embeddings side-by-side and letting human judgment decide which embedding is the best. In this work, we propose a quantitative way of evaluating embeddings, that nonetheless places human perception at the center. We run a comparative study, where we ask people to select "good" and "misleading" views between scatterplots of low-dimensional embeddings of image datasets, simulating the way people usually select embeddings. We use the study data as labels for a set of quality metrics for a supervised machine learning model whose purpose is to discover and quantify what exactly people are looking for when deciding between embeddings. With the model as a proxy for human judgments, we use it to rank embeddings on new datasets, explain why they are relevant, and quantify the degree of subjectivity when people select preferred embeddings.

3.
Artigo em Inglês | MEDLINE | ID: mdl-35254986

RESUMO

We present RagRug, an open-source toolkit for situated analytics. The abilities of RagRug go beyond previous immersive analytics toolkits by focusing on specific requirements emerging when using augmented reality (AR) rather than virtual reality. RagRug combines state of the art visual encoding capabilities with a comprehensive physical-virtual model, which lets application developers systematically describe the physical objects in the real world and their role in AR. We connect AR visualization with data streams from the Internet of Things using distributed dataflow. To this aim, we use reactive programming patterns so that visualizations become context-aware, i.e., they adapt to events coming in from the environment. The resulting authoring system is low-code; it emphasises describing the physical and the virtual world and the dataflow between the elements contained therein. We describe the technical design and implementation of RagRug, and report on five example applications illustrating the toolkit's abilities.

4.
IEEE Comput Graph Appl ; 42(2): 10-20, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-35139011

RESUMO

Our built world is one of the most important factors for a livable future, accounting for massive impact on resource and energy use, as well as climate change, but also the social and economic aspects that come with population growth. The architecture, engineering, and construction industry is facing the challenge that it needs to substantially increase its productivity, let alone the quality of buildings of the future. In this article, we discuss these challenges in more detail, focusing on how digitization can facilitate this transformation of the industry, and link them to opportunities for visualization and augmented reality research. We illustrate solution strategies for advanced building systems based on wood and fiber.


Assuntos
Indústria da Construção , Engenharia , Previsões
5.
IEEE Trans Vis Comput Graph ; 28(1): 11-21, 2022 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-34587040

RESUMO

In this design study, we present IRVINE, a Visual Analytics (VA) system, which facilitates the analysis of acoustic data to detect and understand previously unknown errors in the manufacturing of electrical engines. In serial manufacturing processes, signatures from acoustic data provide valuable information on how the relationship between multiple produced engines serves to detect and understand previously unknown errors. To analyze such signatures, IRVINE leverages interactive clustering and data labeling techniques, allowing users to analyze clusters of engines with similar signatures, drill down to groups of engines, and select an engine of interest. Furthermore, IRVINE allows to assign labels to engines and clusters and annotate the cause of an error in the acoustic raw measurement of an engine. Since labels and annotations represent valuable knowledge, they are conserved in a knowledge database to be available for other stakeholders. We contribute a design study, where we developed IRVINE in four main iterations with engineers from a company in the automotive sector. To validate IRVINE, we conducted a field study with six domain experts. Our results suggest a high usability and usefulness of IRVINE as part of the improvement of a real-world manufacturing process. Specifically, with IRVINE domain experts were able to label and annotate produced electrical engines more than 30% faster.

6.
IEEE Comput Graph Appl ; 41(4): 125-132, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34264822

RESUMO

In recent years, research on immersive environments has experienced a new wave of interest, and immersive analytics has been established as a new research field. Every year, a vast amount of different techniques, applications, and user studies are published that focus on employing immersive environments for visualizing and analyzing data. Nevertheless, immersive analytics is still a relatively unexplored field that needs more basic research in many aspects and is still viewed with skepticism. Rightly so, because in our opinion, many researchers do not fully exploit the possibilities offered by immersive environments and, on the contrary, sometimes even overestimate the power of immersive visualizations. Although a growing body of papers has demonstrated individual advantages of immersive analytics for specific tasks and problems, the general benefit of using immersive environments for effective analytic tasks remains controversial. In this article, we reflect on when and how immersion may be appropriate for the analysis and present four guiding scenarios. We report on our experiences, discuss the landscape of assessment strategies, and point out the directions where we believe immersive visualizations have the greatest potential.

7.
IEEE Trans Vis Comput Graph ; 27(9): 3826-3833, 2021 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-33502982

RESUMO

We present the VIS30K dataset, a collection of 29,689 images that represents 30 years of figures and tables from each track of the IEEE Visualization conference series (Vis, SciVis, InfoVis, VAST). VIS30K's comprehensive coverage of the scientific literature in visualization not only reflects the progress of the field but also enables researchers to study the evolution of the state-of-the-art and to find relevant work based on graphical content. We describe the dataset and our semi-automatic collection process, which couples convolutional neural networks (CNN) with curation. Extracting figures and tables semi-automatically allows us to verify that no images are overlooked or extracted erroneously. To improve quality further, we engaged in a peer-search process for high-quality figures from early IEEE Visualization papers. With the resulting data, we also contribute VISImageNavigator (VIN, visimagenavigator.github.io), a web-based tool that facilitates searching and exploring VIS30K by author names, paper keywords, title and abstract, and years.

8.
IEEE Comput Graph Appl ; 41(6): 101-110, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-32746086

RESUMO

Simulations of cosmic evolution are a means to explain the formation of the universe as we see it today. The resulting data of such simulations comprise numerous physical quantities, which turns their analysis into a complex task. Here, we analyze such high-dimensional and time-varying particle data using various visualization techniques from the fields of particle visualization, flow visualization, volume visualization, and information visualization. Our approach employs specialized filters to extract and highlight the development of so-called active galactic nuclei and filament structures formed by the particles. Additionally, we calculate X-ray emission of the evolving structures in a preprocessing step to complement visual analysis. Our approach is integrated into a single visual analytics framework to allow for analysis of star formation at interactive frame rates. Finally, we lay out the methodological aspects of our work that led to success at the 2019 IEEE SciVis Contest.

9.
IEEE Trans Vis Comput Graph ; 27(2): 1634-1643, 2021 Feb.
Artigo em Inglês | MEDLINE | ID: mdl-33048718

RESUMO

In this paper, we propose SineStream, a new variant of streamgraphs that improves their readability by minimizing sine illusion effects. Such effects reflect the tendency of humans to take the orthogonal rather than the vertical distance between two curves as their distance. In SineStream, we connect the readability of streamgraphs with minimizing sine illusions and by doing so provide a perceptual foundation for their design. As the geometry of a streamgraph is controlled by its baseline (the bottom-most curve) and the ordering of the layers, we re-interpret baseline computation and layer ordering algorithms in terms of reducing sine illusion effects. For baseline computation, we improve previous methods by introducing a Gaussian weight to penalize layers with large thickness changes. For layer ordering, three design requirements are proposed and implemented through a hierarchical clustering algorithm. Quantitative experiments and user studies demonstrate that SineStream improves the readability and aesthetics of streamgraphs compared to state-of-the-art methods.

10.
IEEE Trans Vis Comput Graph ; 27(2): 475-484, 2021 Feb.
Artigo em Inglês | MEDLINE | ID: mdl-33048720

RESUMO

We present an integrated approach for creating and assigning color palettes to different visualizations such as multi-class scatterplots, line, and bar charts. While other methods separate the creation of colors from their assignment, our approach takes data characteristics into account to produce color palettes, which are then assigned in a way that fosters better visual discrimination of classes. To do so, we use a customized optimization based on simulated annealing to maximize the combination of three carefully designed color scoring functions: point distinctness, name difference, and color discrimination. We compare our approach to state-of-the-art palettes with a controlled user study for scatterplots and line charts, furthermore we performed a case study. Our results show that Palettailor, as a fully-automated approach, generates color palettes with a higher discrimination quality than existing approaches. The efficiency of our optimization allows us also to incorporate user modifications into the color selection process.

11.
IEEE Trans Vis Comput Graph ; 27(2): 1204-1213, 2021 02.
Artigo em Inglês | MEDLINE | ID: mdl-33055033

RESUMO

Ubiquitous, situated, and physical visualizations create entirely new possibilities for tasks contextualized in the real world, such as doctors inserting needles. During the development of situated visualizations, evaluating visualizations is a core requirement. However, performing such evaluations is intrinsically hard as the real scenarios are safety-critical or expensive to test. To overcome these issues, researchers and practitioners adapt classical approaches from ubiquitous computing and use surrogate empirical methods such as Augmented Reality (AR), Virtual Reality (VR) prototypes, or merely online demonstrations. This approach's primary assumption is that meaningful insights can also be gained from different, usually cheaper and less cumbersome empirical methods. Nevertheless, recent efforts in the Human-Computer Interaction (HCI) community have found evidence against this assumption, which would impede the use of surrogate empirical methods. Currently, these insights rely on a single investigation of four interactive objects. The goal of this work is to investigate if these prior findings also hold for situated visualizations. Therefore, we first created a scenario where situated visualizations support users in do-it-yourself (DIY) tasks such as crafting and assembly. We then set up five empirical study methods to evaluate the four tasks using an online survey, as well as VR, AR, laboratory, and in-situ studies. Using this study design, we conducted a new study with 60 participants. Our results show that the situated visualizations we investigated in this study are not prone to the same dependency on the empirical method, as found in previous work. Our study provides the first evidence that analyzing situated visualizations through different empirical (surrogate) methods might lead to comparable results.

12.
IEEE Trans Vis Comput Graph ; 26(1): 759-769, 2020 01.
Artigo em Inglês | MEDLINE | ID: mdl-31443018

RESUMO

In this paper, we examine the robustness of scagnostics through a series of theoretical and empirical studies. First, we investigate the sensitivity of scagnostics by employing perturbing operations on more than 60M synthetic and real-world scatterplots. We found that two scagnostic measures, Outlying and Clumpy, are overly sensitive to data binning. To understand how these measures align with human judgments of visual features, we conducted a study with 24 participants, which reveals that i) humans are not sensitive to small perturbations of the data that cause large changes in both measures, and ii) the perception of clumpiness heavily depends on per-cluster topologies and structures. Motivated by these results, we propose Robust Scagnostics (RScag) by combining adaptive binning with a hierarchy-based form of scagnostics. An analysis shows that RScag improves on the robustness of original scagnostics, aligns better with human judgments, and is equally fast as the traditional scagnostic measures.

13.
Artigo em Inglês | MEDLINE | ID: mdl-30136962

RESUMO

Traditional fisheye views for exploring large graphs introduce substantial distortions that often lead to a decreased readability of paths and other interesting structures. To overcome these problems, we propose a framework for structure-aware fisheye views. Using edge orientations as constraints for graph layout optimization allows us not only to reduce spatial and temporal distortions during fisheye zooms, but also to improve the readability of the graph structure. Furthermore, the framework enables us to optimize fisheye lenses towards specific tasks and design a family of new lenses: polyfocal, cluster, and path lenses. A GPU implementation lets us process large graphs with up to 15,000 nodes at interactive rates. A comprehensive evaluation, a user study, and two case studies demonstrate that our structure-aware fisheye views improve layout readability and user performance.

14.
Artigo em Inglês | MEDLINE | ID: mdl-30136963

RESUMO

Appropriate choice of colors significantly aids viewers in understanding the structures in multiclass scatterplots and becomes more important with a growing number of data points and groups. An appropriate color mapping is also an important parameter for the creation of an aesthetically pleasing scatterplot. Currently, users of visualization software routinely rely on color mappings that have been pre-defined by the software. A default color mapping, however, cannot ensure an optimal perceptual separability between groups, and sometimes may even lead to a misinterpretation of the data. In this paper, we present an effective approach for color assignment based on a set of given colors that is designed to optimize the perception of scatterplots. Our approach takes into account the spatial relationships, density, degree of overlap between point clusters, and also the background color. For this purpose, we use a genetic algorithm that is able to efficiently find good color assignments. We implemented an interactive color assignment system with three extensions of the basic method that incorporates top K suggestions, user-defined color subsets, and classes of interest for the optimization. To demonstrate the effectiveness of our assignment technique, we conducted a numerical study and a controlled user study to compare our approach with default color assignments; our findings were verified by two expert studies. The results show that our approach is able to support users in distinguishing cluster numbers faster and more precisely than default assignment methods.

15.
IEEE Trans Vis Comput Graph ; 24(5): 1828-1840, 2018 05.
Artigo em Inglês | MEDLINE | ID: mdl-28489538

RESUMO

Dimensionality reduction (DR) is a common strategy for visual analysis of labeled high-dimensional data. Low-dimensional representations of the data help, for instance, to explore the class separability and the spatial distribution of the data. Widely-used unsupervised DR methods like PCA do not aim to maximize the class separation, while supervised DR methods like LDA often assume certain spatial distributions and do not take perceptual capabilities of humans into account. These issues make them ineffective for complicated class structures. Towards filling this gap, we present a perception-driven linear dimensionality reduction approach that maximizes the perceived class separation in projections. Our approach builds on recent developments in perception-based separation measures that have achieved good results in imitating human perception. We extend these measures to be density-aware and incorporate them into a customized simulated annealing algorithm, which can rapidly generate a near optimal DR projection. We demonstrate the effectiveness of our approach by comparing it to state-of-the-art DR methods on 93 datasets, using both quantitative measure and human judgments. We also provide case studies with class-imbalanced and unlabeled data.

16.
IEEE Trans Vis Comput Graph ; 24(1): 584-594, 2018 01.
Artigo em Inglês | MEDLINE | ID: mdl-28866525

RESUMO

We investigate priming and anchoring effects on perceptual tasks in visualization. Priming or anchoring effects depict the phenomena that a stimulus might influence subsequent human judgments on a perceptual level, or on a cognitive level by providing a frame of reference. Using visual class separability in scatterplots as an example task, we performed a set of five studies to investigate the potential existence of priming and anchoring effects. Our findings show that-under certain circumstances-such effects indeed exist. In other words, humans judge class separability of the same scatterplot differently depending on the scatterplot(s) they have seen before. These findings inform future work on better understanding and more accurately modeling human perception of visual patterns.


Assuntos
Viés , Testes Psicológicos , Priming de Repetição/fisiologia , Percepção Visual/fisiologia , Crowdsourcing , Bases de Dados Factuais , Humanos , Modelos Psicológicos , Projetos de Pesquisa
17.
IEEE Trans Vis Comput Graph ; 24(1): 298-308, 2018 01.
Artigo em Inglês | MEDLINE | ID: mdl-28866560

RESUMO

Labeling data instances is an important task in machine learning and visual analytics. Both fields provide a broad set of labeling strategies, whereby machine learning (and in particular active learning) follows a rather model-centered approach and visual analytics employs rather user-centered approaches (visual-interactive labeling). Both approaches have individual strengths and weaknesses. In this work, we conduct an experiment with three parts to assess and compare the performance of these different labeling strategies. In our study, we (1) identify different visual labeling strategies for user-centered labeling, (2) investigate strengths and weaknesses of labeling strategies for different labeling tasks and task complexities, and (3) shed light on the effect of using different visual encodings to guide the visual-interactive labeling process. We further compare labeling of single versus multiple instances at a time, and quantify the impact on efficiency. We systematically compare the performance of visual interactive labeling with that of active learning. Our main findings are that visual-interactive labeling can outperform active learning, given the condition that dimension reduction separates well the class distributions. Moreover, using dimension reduction in combination with additional visual encodings that expose the internal state of the learning model turns out to improve the performance of visual-interactive labeling.

18.
IEEE Trans Vis Comput Graph ; 24(1): 647-656, 2018 01.
Artigo em Inglês | MEDLINE | ID: mdl-28866587

RESUMO

We present EdWordle, a method for consistently editing word clouds. At its heart, EdWordle allows users to move and edit words while preserving the neighborhoods of other words. To do so, we combine a constrained rigid body simulation with a neighborhood-aware local Wordle algorithm to update the cloud and to create very compact layouts. The consistent and stable behavior of EdWordle enables users to create new forms of word clouds such as storytelling clouds in which the position of words is carefully edited. We compare our approach with state-of-the-art methods and show that we can improve user performance, user satisfaction, as well as the layout itself.

19.
IEEE Trans Vis Comput Graph ; 24(1): 489-499, 2018 01.
Artigo em Inglês | MEDLINE | ID: mdl-28866589

RESUMO

We present an improved stress majorization method that incorporates various constraints, including directional constraints without the necessity of solving a constraint optimization problem. This is achieved by reformulating the stress function to impose constraints on both the edge vectors and lengths instead of just on the edge lengths (node distances). This is a unified framework for both constrained and unconstrained graph visualizations, where we can model most existing layout constraints, as well as develop new ones such as the star shapes and cluster separation constraints within stress majorization. This improvement also allows us to parallelize computation with an efficient GPU conjugant gradient solver, which yields fast and stable solutions, even for large graphs. As a result, we allow the constraint-based exploration of large graphs with 10K nodes - an approach which previous methods cannot support.

20.
IEEE Trans Vis Comput Graph ; 23(9): 2199-2206, 2017 09.
Artigo em Inglês | MEDLINE | ID: mdl-28113510

RESUMO

We have created and made available to all a dataset with information about every paper that has appeared at the IEEE Visualization (VIS) set of conferences: InfoVis, SciVis, VAST, and Vis. The information about each paper includes its title, abstract, authors, and citations to other papers in the conference series, among many other attributes. This article describes the motivation for creating the dataset, as well as our process of coalescing and cleaning the data, and a set of three visualizations we created to facilitate exploration of the data. This data is meant to be useful to the broad data visualization community to help understand the evolution of the field and as an example document collection for text data visualization research.

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