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1.
Artigo em Inglês | MEDLINE | ID: mdl-35679374

RESUMO

We propose Composite Parallel Coordinates, a novel parallel coordinates technique to effectively represent the interplay of component alternatives in a system. It builds upon a dedicated data model that formally describes the interaction of components. Parallel coordinates can help decision-makers identify the most preferred solution among a number of alternatives. Multi-component systems require one such multi-attribute choice for each component. Each of these choices might have side effects on the system's operability and performance, making them co-dependent. Common approaches employ complex multi-component models or involve back-and-forth iterations between single components until an acceptable compromise is reached. A simultaneous visual exploration across independently modeled but connected components is needed to make system design more efficient. Using dedicated layout and interaction strategies, our Composite Parallel Coordinates allow analysts to explore both individual properties of components as well as their interoperability and joint performance. We showcase the effectiveness of Composite Parallel Coordinates for co-dependent multi-attribute choices by means of three real-world scenarios from distinct application areas. In addition to the case studies, we reflect on observing two domain experts collaboratively working with the proposed technique and communicating along the way.

2.
Comput Graph ; 106: 1-8, 2022 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-35637696

RESUMO

A major challenge for departments of public health (DPHs) in dealing with the ongoing COVID-19 pandemic is tracing contacts in exponentially growing SARS-CoV-2 infection clusters. Prevention of further disease spread requires a comprehensive registration of the connections between individuals and clusters. Due to the high number of infections with unknown origin, the healthcare analysts need to identify connected cases and clusters through accumulated epidemiological knowledge and the metadata of the infections in their database. Here we contribute a visual analytics dashboard to identify, assess and visualize clusters in COVID-19 contact tracing networks. Additionally, we demonstrate how graph-based machine learning methods can be used to find missing links between infection clusters and thus support the mission to get a comprehensive view on infection events. This work was developed through close collaboration with DPHs in Germany. We argue how our dashboard supports the identification of clusters by public health experts, discuss ongoing developments and possible extensions.

3.
IEEE Trans Vis Comput Graph ; 28(9): 3070-3081, 2022 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-33434130

RESUMO

Event sequences are central to the analysis of data in domains that range from biology and health, to logfile analysis and people's everyday behavior. Many visualization tools have been created for such data, but people are error-prone when asked to judge the similarity of event sequences with basic presentation methods. This article describes an experiment that investigates whether local and global alignment techniques improve people's performance when judging sequence similarity. Participants were divided into three groups (basic versus local versus global alignment), and each participant judged the similarity of 180 sets of pseudo-randomly generated sequences. Each set comprised a target, a correct choice and a wrong choice. After training, the global alignment group was more accurate than the local alignment group (98 versus 93 percent correct), with the basic group getting 95 percent correct. Participants' response times were primarily affected by the number of event types, the similarity of sequences (measured by the Levenshtein distance) and the edit types (nine combinations of deletion, insertion and substitution). In summary, global alignment is superior and people's performance could be further improved by choosing alignment parameters that explicitly penalize sequence mismatches.


Assuntos
Algoritmos , Gráficos por Computador , Humanos , Alinhamento de Sequência
4.
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
5.
IEEE Trans Vis Comput Graph ; 27(2): 1840-1849, 2021 02.
Artigo em Inglês | MEDLINE | ID: mdl-33136543

RESUMO

Online services are used for all kinds of activities, like news, entertainment, publishing content or connecting with others. But information technology enables new threats to privacy by means of global mass surveillance, vast databases and fast distribution networks. Current news are full of misuses and data leakages. In most cases, users are powerless in such situations and develop an attitude of neglect for their online behaviour. On the other hand, the GDPR (General Data Protection Regulation) gives users the right to request a copy of all their personal data stored by a particular service, but the received data is hard to understand or analyze by the common internet user. This paper presents TransparencyVis - a web-based interface to support the visual and interactive exploration of data exports from different online services. With this approach, we aim at increasing the awareness of personal data stored by such online services and the effects of online behaviour. This design study provides an online accessible prototype and a best practice to unify data exports from different sources.

6.
IEEE Trans Vis Comput Graph ; 25(3): 1615-1628, 2019 03.
Artigo em Inglês | MEDLINE | ID: mdl-29994364

RESUMO

In this design study, we present a visualization technique that segments patients' histories instead of treating them as raw event sequences, aggregates the segments using criteria such as the whole history or treatment combinations, and then visualizes the aggregated segments as static dashboards that are arranged in a dashboard network to show longitudinal changes. The static dashboards were developed in nine iterations, to show 15 important attributes from the patients' histories. The final design was evaluated with five non-experts, five visualization experts and four medical experts, who successfully used it to gain an overview of a 2,000 patient dataset, and to make observations about longitudinal changes and differences between two cohorts. The research represents a step-change in the detail of large-scale data that may be successfully visualized using dashboards, and provides guidance about how the approach may be generalized.


Assuntos
Gráficos por Computador , Registros Eletrônicos de Saúde , Informática Médica/métodos , Neoplasias da Próstata , Humanos , Masculino , Neoplasias da Próstata/diagnóstico , Neoplasias da Próstata/patologia , Neoplasias da Próstata/fisiopatologia , Neoplasias da Próstata/cirurgia , Interface Usuário-Computador
8.
IEEE Trans Vis Comput Graph ; 19(12): 2257-66, 2013 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-24051792

RESUMO

We present MotionExplorer, an exploratory search and analysis system for sequences of human motion in large motion capture data collections. This special type of multivariate time series data is relevant in many research fields including medicine, sports and animation. Key tasks in working with motion data include analysis of motion states and transitions, and synthesis of motion vectors by interpolation and combination. In the practice of research and application of human motion data, challenges exist in providing visual summaries and drill-down functionality for handling large motion data collections. We find that this domain can benefit from appropriate visual retrieval and analysis support to handle these tasks in presence of large motion data. To address this need, we developed MotionExplorer together with domain experts as an exploratory search system based on interactive aggregation and visualization of motion states as a basis for data navigation, exploration, and search. Based on an overview-first type visualization, users are able to search for interesting sub-sequences of motion based on a query-by-example metaphor, and explore search results by details on demand. We developed MotionExplorer in close collaboration with the targeted users who are researchers working on human motion synthesis and analysis, including a summative field study. Additionally, we conducted a laboratory design study to substantially improve MotionExplorer towards an intuitive, usable and robust design. MotionExplorer enables the search in human motion capture data with only a few mouse clicks. The researchers unanimously confirm that the system can efficiently support their work.


Assuntos
Actigrafia/métodos , Algoritmos , Gráficos por Computador , Bases de Dados Factuais , Movimento/fisiologia , Software , Interface Usuário-Computador , Interpretação Estatística de Dados , Humanos , Armazenamento e Recuperação da Informação/métodos
9.
IEEE Comput Graph Appl ; 33(4): 14-9, 2013.
Artigo em Inglês | MEDLINE | ID: mdl-24808055

RESUMO

The past 10 years have seen profound changes in visualization algorithms, techniques, methodologies, and applications. These changes are forcing alterations to visualization courses. Unfortunately, outdated course content recommendations, together with profound changes in the underlying technology and methodology, are producing an unstable ground for educators at a time when visual representations are becoming increasingly important. To address this issue, educators held meetings or workshops at Siggraph 2011 and 2012 and a panel and workshop at Eurographics 2012. This article presents the insights gathered at these events.

10.
IEEE Comput Graph Appl ; 32(5): 84-9, 2012.
Artigo em Inglês | MEDLINE | ID: mdl-24806990

RESUMO

This article looks at the current and future roles of information visualization, semantics visualization, and visual analytics in policy modeling. Many experts believe that you can't overestimate visualization's role in this respect.


Assuntos
Gráficos por Computador , Modelos Organizacionais , Formulação de Políticas , Humanos , Interface Usuário-Computador
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