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1.
IEEE Comput Graph Appl ; 42(5): 84-89, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-36194699

RESUMO

In this article, we present a digital platform for unmanned traffic management, UTM City, for research on visualization, simulation, and management of autonomous urban vehicle traffic. Such vehicles orient themselves automatically and provide services ranging from transport to remote presence and surveillance, and new regulations and standards for authorization and monitoring are currently being developed to accommodate for such services. Our system has been developed in close collaboration with domain experts that have contributed with scenarios and participated in numerous workshops to explore the use of visualization in airborne drone traffic monitoring, management, and development of the air space. We share here our experiences with this system and explore the need for visualization in future scenarios to ensure safe, free, and efficient air spaces.

2.
IEEE Comput Graph Appl ; 42(4): 114-119, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-35839167

RESUMO

Scientific visualization is a key approach to understanding the growing massive streams of data from scientific simulations and experiments. In this article, I review technology trends including the positive effects of Moore's law on science, the significant gap between processing and data storage speeds, the emergence of hardware accelerators for ray-tracing, and the availability of robust machine learning techniques. These trends represent changes to the status quo and present the scientific visualization community with a new set of challenges. A major challenge involves extending our approaches to visualize the modern scientific process, which includes scientific verification and validation. Another key challenge to the community is the growing number, size, and complexity of scientific datasets. A final challenge is to take advantage of emerging technology trends in custom hardware and machine learning to significantly improve the large-scale data visualization process.


Assuntos
Armazenamento e Recuperação da Informação , Aprendizado de Máquina , Tecnologia
3.
IEEE Comput Graph Appl ; 42(3): 29-38, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-35671279

RESUMO

In this Viewpoint article, we describe the persistent tensions between various camps on the "right" way to conduct evaluations in visualization. Visualization as a field is the amalgamation of cognitive and perceptual sciences and computer graphics, among others. As a result, the relatively disjointed lineages in visualization understandably approach the topic of evaluation very differently. It is both a blessing and a curse to our field. It is a blessing, because the collaboration of diverse perspectives is the breeding ground of innovation. Yet it is a curse, because as a community, we have yet to resolve an appreciation for differing perspectives on the topic of evaluation. We explicate these differing expectations and conventions to appreciate the spectrum of evaluation design decisions. We describe some guiding questions that researchers may consider when designing evaluations to navigate differing readers' evaluation expectations.


Assuntos
Gráficos por Computador , Projetos de Pesquisa
4.
IEEE Comput Graph Appl ; 42(2): 110-114, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-35417344

RESUMO

Encoding data visually is at the heart of visualization. We usually assume that encodings are read as specified (i.e., if a bar chart is drawn by the length of the bars based on the data, that is also how we read them). In this paper, we question this assumption and demonstrate that observed encodings often differ from the ones used to specify the visualization. The value of a chart also often comes from higher level derived encodings, and which encodings end up getting used also depends on the user's task.

5.
IEEE Comput Graph Appl ; 42(1): 123-133, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-35077350

RESUMO

We introduce a new research area in visual analytics (VA) aiming to bridge existing gaps between methods of interactive machine learning (ML) and eXplainable Artificial Intelligence (XAI), on one side, and human minds, on the other side. The gaps are, first, a conceptual mismatch between ML/XAI outputs and human mental models and ways of reasoning, and second, a mismatch between the information quantity and level of detail and human capabilities to perceive and understand. A grand challenge is to adapt ML and XAI to human goals, concepts, values, and ways of thinking. Complementing the current efforts in XAI towards solving this challenge, VA can contribute by exploiting the potential of visualization as an effective way of communicating information to humans and a strong trigger of human abstractive perception and thinking. We propose a cross-disciplinary research framework and formulate research directions for VA.


Assuntos
Inteligência Artificial , Aprendizado de Máquina , Humanos
6.
IEEE Comput Graph Appl ; 41(6): 7-12, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34890313

RESUMO

The increasing use of artificial intelligence (AI) technologies across application domains has prompted our society to pay closer attention to AI's trustworthiness, fairness, interpretability, and accountability. In order to foster trust in AI, it is important to consider the potential of interactive visualization, and how such visualizations help build trust in AI systems. This manifesto discusses the relevance of interactive visualizations and makes the following four claims: i) trust is not a technical problem, ii) trust is dynamic, iii) visualization cannot address all aspects of trust, and iv) visualization is crucial for human agency in AI.


Assuntos
Inteligência Artificial , Confiança , Humanos , Responsabilidade Social
7.
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.

8.
PLoS Comput Biol ; 17(4): e1008901, 2021 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-33822781

RESUMO

[This corrects the article DOI: 10.1371/journal.pcbi.1008259.].

9.
IEEE Comput Graph Appl ; 41(2): 8-16, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-33729921

RESUMO

We argue that visualization research has overwhelmingly focused on users from the economically developed world. However, billions of people around the world are rapidly emerging as new users of information technology. Most of the next billion users of visualization technologies will come from parts of the world that are extremely populous but historically ignored by the visualization research community. Their needs may be different to the types of users that researchers have targeted in the past, but, at the same time, they may have even more to gain in terms of access to data potentially affecting their quality of life. We propose a call to action for the visualization community to identify opportunities and use cases where users can benefit from visualization; develop universal design principles; extend evaluations by including the general population; and engage with a wider global population.

10.
IEEE Comput Graph Appl ; 41(1): 8-14, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-33444127

RESUMO

Public awareness and concern about climate change often do not match the magnitude of its threat to humans and our environment. One reason for this disagreement is that it is difficult to mentally simulate the effects of a process as complex as climate change and to have a concrete representation of the impact that our individual actions will have on our own future, especially if the consequences are long term and abstract. To overcome these challenges, we propose to use cutting-edge artificial intelligence (AI) approaches to develop an interactive personalized visualization tool, the AI climate impact visualizer. It will allow a user to enter an address-be it their house, their school, or their workplace--and it will provide them with an AI-imagined possible visualization of the future of this location in 2050 following the detrimental effects of climate change such as floods, storms, and wildfires. This image will be accompanied by accessible information regarding the science behind climate change, i.e., why extreme weather events are becoming more frequent and what kinds of changes are happening on a local and global scale.

11.
IEEE Comput Graph Appl ; 41(5): 7-15, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34506269

RESUMO

The medical domain has been an inspiring application area in visualization research for many years already, but many open challenges remain. The driving forces of medical visualization research have been strengthened by novel developments, for example, in deep learning, the advent of affordable VR technology, and the need to provide medical visualizations for broader audiences. At IEEE VIS 2020, we hosted an Application Spotlight session to highlight recent medical visualization research topics. With this article, we provide the visualization community with ten such open challenges, primarily focused on challenges related to the visualization of medical imaging data. We first describe the unique nature of medical data in terms of data preparation, access, and standardization. Subsequently, we cover open visualization research challenges related to uncertainty, multimodal and multiscale approaches, and evaluation. Finally, we emphasize challenges related to users focusing on explainable AI, immersive visualization, P4 medicine, and narrative visualization.

13.
IEEE Comput Graph Appl ; 40(6): 88-96, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-33095702

RESUMO

In interactive visual machine learning (IVML), humans and machine learning algorithms collaborate to achieve tasks mediated by interactive visual interfaces. This human-in-the-loop approach to machine learning brings forth not only numerous intelligibility, trust, and usability issues, but also many open questions with respect to the evaluation of the IVML system, both as separate components, and as a holistic entity that includes both human and machine intelligence. This article describes the challenges and research gaps identified in an IEEE VIS workshop on the evaluation of IVML systems.

14.
IEEE Comput Graph Appl ; 40(5): 108-119, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32833626

RESUMO

The American National Academies of Sciences, Engineering, and Medicine (NASEM) has recently released the report "Reproducibility and Replicability in Science." The report has prompted discussions within many disciplines about the extent of the current adoption of reproducibility and replicability, the challenges involved in publishing reproducible results as well as strategies for improving. We organized a panel at the IEEE VIS conference 2019 to start a discussion on the reproducibility challenges faced by the visualization community and how those challenges might be addressed. In this viewpoint, we summarize key findings of the NASEM report, the panel discussion, and outline a set of recommendations for the visualization community.

15.
IEEE Comput Graph Appl ; 40(4): 84-95, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32540790

RESUMO

Visualizations produced by collaborations between artists, scientists, and visualization experts lay claim to being not only more effective in delivering information but also more effective in their abilities to elicit qualities like human connection. However, as prior work in the visualization community has demonstrated, it is difficult to evaluate these claims because characteristics associated with human connection are not easily measured quantitatively. In this Visualization Viewpoints piece, we address this problem in the context of our work to develop methods of evaluating visualizations created by Sculpting Visualization, a multidisciplinary project that incorporates art and design theory and practice into the process of scientific visualization. We present the design and results of a study in which we used close reading, a formal methodology used by humanities scholars, as a way to test reactions and analyses from evaluation participants related to an image created using Sculpting Visualization. In addition to specific suggestions about how to improve future iterations of the visualization, we discuss key findings of the evaluation related to contextual information, visual perspective, and associations that individual viewers brought to bear on their experience with the visualization.


Assuntos
Gráficos por Computador , Visualização de Dados , Escultura , Encéfalo/diagnóstico por imagem , Ciências da Terra , Golfo do México , Humanos , Modelos Teóricos , Neuroimagem
16.
IEEE Comput Graph Appl ; 40(3): 73-82, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32356729

RESUMO

Interactive data exploration and analysis is an inherently personal process. One's background, experience, interests, cognitive style, personality, and other sociotechnical factors often shape such a process, as well as the provenance of exploring, analyzing, and interpreting data. This Viewpoint posits both what personal information and how such personal information could be taken into account to design more effective visual analytic systems, a valuable and under-explored direction.

17.
IEEE Comput Graph Appl ; 40(2): 82-90, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32149613

RESUMO

The visualization research community can and should reach broader audiences beyond data-savvy groups of people, because these audiences could also greatly benefit from visual access to data. In this article, we discuss four research topics-personal data visualization, data visualization on mobile devices, inclusive data visualization, and multimodal interaction for data visualization-that, individually and collaboratively, would help us reach broader audiences with data visualization, making data more accessible.

18.
IEEE Comput Graph Appl ; 40(1): 90-98, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-31944943

RESUMO

Scientific users present unique challenges to visualization researchers. Their high-level tasks require them to apply domain-specific expertise. We introduce a broader audience to the CinemaScience project and demonstrate how CinemaScience enables efficient visualization workflows that can bring in scientist expertise and drive scientific insight.

19.
IEEE Comput Graph Appl ; 39(6): 76-85, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-31714213

RESUMO

In situ visualization is an increasingly important approach for computational science, as it can address limitations on leading edge high-performance computers and also can provide an increased spatio-temporal resolution. However, there are many open research issues with effective in situ processing. This article describes the challenges identified by a recent Dagstuhl Seminar on the topic.

20.
IEEE Comput Graph Appl ; 39(5): 8-17, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-31442961

RESUMO

When assessing the value of visualizations, researchers traditionally focus on efficiency, comprehension, or insight. However, analyzing successful data physicalizations leads to a deep appreciation for hedonic qualities. Informed by the role of emotion in psychology, art, design, marketing, and HCI, we argue for an expanded definition of value, applicable to all forms of data visualization.

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