Characterizing Guidance in Visual Analytics

Ceneda, Davide; Gschwandtner, Theresia; May, Thorsten; Miksch, Silvia; Schulz, Hans-Jörg; Streit, Marc; Tominski, Christian

doi:10.1109/tvcg.2016.2598468

Cited by 192 publications

(185 citation statements)

References 40 publications

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“…While our set of interestingness measures is not exhaustive, based on our insights into the application domain, we provide measures to evaluate dimensions by a) uncertainty, b) degree of change to the value domains, c) variance of the time series, d) original order, and e) the alphabetical order (to ease analyses addressing the semantic meanings). The scores of the ranked degree‐of‐interest functions are used as directive guidance [CGM∗17] to MVTS dimensions and allow the effective dimension selection. In the juxtaposed visualization of dimensions in Figure 4, the variance criterion was used for the dimension selection (high variances at the top).…”

Section: Approachmentioning

confidence: 99%

Visual‐Interactive Preprocessing of Multivariate Time Series Data

Bernard

Hutter

Reinemuth

et al. 2019

Computer Graphics Forum

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Pre‐processing is a prerequisite to conduct effective and efficient downstream data analysis. Pre‐processing pipelines often require multiple routines to address data quality challenges and to bring the data into a usable form. For both the construction and the refinement of pre‐processing pipelines, human‐in‐the‐loop approaches are highly beneficial. This particularly applies to multivariate time series, a complex data type with multiple values developing over time. Due to the high specificity of this domain, it has not been subject to in‐depth research in visual analytics. We present a visual‐interactive approach for preprocessing multivariate time series data with the following aspects. Our approach supports analysts to carry out six core analysis tasks related to pre‐processing of multivariate time series. To support these tasks, we identify requirements to baseline toolkits that may help practitioners in their choice. We characterize the space of visualization designs for uncertainty‐aware pre‐processing and justify our decisions. Two usage scenarios demonstrate applicability of our approach, design choices, and uncertainty visualizations for the six analysis tasks. This work is one step towards strengthening the visual analytics support for data pre‐processing in general and for uncertainty‐aware pre‐processing of multivariate time series in particular.

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Section: Approachmentioning

confidence: 99%

Visual‐Interactive Preprocessing of Multivariate Time Series Data

Bernard

Hutter

Reinemuth

et al. 2019

Computer Graphics Forum

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“…Additionally, as Visual Analytics solutions tend to be complex, target users are not able to fully exploit their potential. Guided Visual Analytics is a concept that puts emphasis on the effective use of such systems by domain experts the domain of Guided Visual Analytics [CGM*17]. Future research towards these directions would be an interesting enhancement to current work.…”

Section: Discussionmentioning

confidence: 99%

“…Future directions should also revolve around the topic of intuitiveness . There are a lot of processes that are not intuitively obvious, especially with the incorporation of concepts from Artificial Intelligence (AI), such as Deep Learning algorithms [SPG14, CGM*17,PHG*18]. A vast majority of these processes are tackled with automatic methods—the results of which, are better or, at least, not anymore differentiable from a human recommendation.…”

Section: Outlook From the Rt Domainmentioning

confidence: 99%

State‐of‐the‐Art Report: Visual Computing in Radiation Therapy Planning

Schlachter

Raidou

Muren

et al. 2019

Computer Graphics Forum

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Radiation therapy (RT) is one of the major curative approaches for cancer. It is a complex and risky treatment approach, which requires precise planning, prior to the administration of the treatment. Visual Computing (VC) is a fundamental component of RT planning, providing solutions in all parts of the process—from imaging to delivery. Despite the significant technological advancements of RT over the last decades, there are still many challenges to address. This survey provides an overview of the compound planning process of RT, and of the ways that VC has supported RT in all its facets. The RT planning process is described to enable a basic understanding in the involved data, users and workflow steps. A systematic categorization and an extensive analysis of existing literature in the joint VC/RT research is presented, covering the entire planning process. The survey concludes with a discussion on lessons learnt, current status, open challenges, and future directions in VC/RT research.

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“…This highlights a need to guide users carefully through the use of visual reports. Recent research has proposed and evaluated new ways of providing such guidance [55]. …”

Section: Discussionmentioning

confidence: 99%

Exploring Genetic Data Across Individuals: Design and Evaluation of a Novel Comparative Report Tool

Westendorf¹,

Shaer²,

Pollalis³

et al. 2018

J Med Internet Res

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BackgroundThe growth in the availability of personal genomic data to nonexperts poses multiple challenges to human-computer interaction research; data are highly sensitive, complex, and have health implications for individuals and families. However, there has been little research on how nonexpert users explore their genomic data.ObjectiveWe focus on how to support nonexperts in exploring and comparing their own personal genomic report with those of other people. We designed and evaluated CrossGenomics, a novel tool for comparing personal genetic reports, which enables exploration of shared and unshared genetic variants. Focusing on communicating comparative impact, rarity, and certainty, we evaluated alternative novel interactive prototypes.MethodsWe conducted 3 user studies. The first focuses on assessing the usability and understandability of a prototype that facilitates the comparison of reports from 2 family members. Following a design iteration, we studied how various prototypes support the comparison of genetic reports of a 4-person family. Finally, we evaluated the needs of early adopters—people who share their genetic reports publicly for comparing their genetic reports with that of others.ResultsIn the first study, sunburst- and Venn-based comparisons of two genomes led to significantly higher domain comprehension, compared with the linear comparison and with the commonly used tabular format. However, results show gaps between objective and subjective comprehension, as sunburst users reported significantly lower perceived understanding and higher levels of confusion than the users of the tabular report. In the second study, users who were allowed to switch between the different comparison views presented higher comprehension levels, as well as more complex reasoning than users who were limited to a single comparison view. In the third study, 35% (17/49) reported learning something new from comparing their own data with another person’s data. Users indicated that filtering and toggling between comparison views were the most useful features.ConclusionsOur findings (1) highlight features and visualizations that show strengths in facilitating user comprehension of genomic data, (2) demonstrate the value of affording users the flexibility to examine the same report using multiple views, and (3) emphasize users’ needs in comparison of genomic data. We conclude with design implications for engaging nonexperts with complex multidimensional genomic data.

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Characterizing Guidance in Visual Analytics

Cited by 192 publications

References 40 publications

Visual‐Interactive Preprocessing of Multivariate Time Series Data

Visual‐Interactive Preprocessing of Multivariate Time Series Data

State‐of‐the‐Art Report: Visual Computing in Radiation Therapy Planning

Exploring Genetic Data Across Individuals: Design and Evaluation of a Novel Comparative Report Tool

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