Towards Design Principles for Visual Analytics in Operations Contexts

Conlen, Matthew; Stalla, Sara; Jin, Chelly; Hendrie, Maggie; Mushkin, Hillary; Lombeyda, Santiago; Davidoff, Scott

doi:10.1145/3173574.3173712

Cited by 5 publications

(2 citation statements)

References 11 publications

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“…Conlen et al [29] provided VA design principles that are mainly data-driven. They give suggestions on how data should be visualized and which interactions should be provided.…”

Section: Workflows To Generate Visual Analyticsmentioning

confidence: 99%

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A Workflow to Systematically Design Uncertainty-Aware Visual Analytics Applications

Maack,

Raith,

Perez

et al. 2023

Preprint

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Visual Analytics (VA) is a paradigm for insight generation by using visual analysis techniques and automated reasoning by transforming data into hypotheses and visualization to extract new insights, feeding them back into the data. This enhances the data until the desired insight is found. Many applications use this principle to provide meaningful mechanisms to assist decision-makers in achieving their goals. This process can be affected by a variety of uncertainties that can interfere with the user decision-making process. Unfortunately, there is no methodical description and handling tool to systematically include uncertainty in VA. We provide a unified workflow to transform the classic VA cycle into an uncertainty-aware Visual Analytics (UAVA) cycle consisting of five steps. Three real-world applications represent examples of the implementation of the UAVA cycle and the described workflow to prove its usability.

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“…Conlen et al [29] provided VA design principles that are mainly data-driven. They give suggestions on how data should be visualized and which interactions should be provided.…”

Section: Workflows To Generate Visual Analyticsmentioning

confidence: 99%

“…In this stage, it is important to note that required data transformation (D W ) operations and the feedback loop (F(S )) are identified. Conlen et al [29] provided a first attempt to provide design rules for VA approaches. At this point, uncertainty is not included in the design process explicitly.…”

Section: Step 1: Design a Visual Analytics Cyclementioning

confidence: 99%

A Workflow to Systematically Design Uncertainty-Aware Visual Analytics Applications

Maack,

Raith,

Perez

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

A workflow to systematically design uncertainty-aware visual analytics applications

Maack,

Raith,

Pérez

et al. 2024

Vis Comput

View full text Add to dashboard Cite

Visual analytics (VA) is a paradigm for insight generation by using visual analysis techniques and automated reasoning by transforming data into hypotheses and visualization to extract new insights. The insights are fed back into the data to enhance it until the desired insight is found. Many applications use this principle to provide meaningful mechanisms to assist decision-makers in achieving their goals. This process can be affected by various uncertainties that can interfere with the user decision-making process. Currently, there are no methodical description and handling tool to include uncertainty in VA systematically. We provide a unified workflow to transform the classic VA cycle into an uncertainty-aware visual analytics (UAVA) cycle consisting of five steps. To prove its usability, three real-world applications represent examples of the UAVA cycle implementation and the described workflow.

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JPL/Caltech ArtCenter

Hendrie

Mushkin

Lombeyda

et al. 2022

IDJ

Self Cite

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Data visualization frequently provides audiences with novel semantic and computational presentations. How does a multifaceted team expand this scope by harnessing the power of visualization as a tool to think with? The NASA JPL/Caltech/ ArtCenter data visualization program demonstrates how scientific knowledge, shaped from data and theory, is equally co-constructed from diverse human perspectives. We will share case studies from Mars Rover Path planning and PIXLISE, a visual reasoning tool for understanding planetary geology. Working from source data through mixed media artifacts, these projects explore co-design methods for complex scientific domains with real-world applications. Our methodology emphasizes that all participants in the co-design process are both learners and experts. In this dynamic, the design and coding process are unique modes of critical discovery.

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Towards Design Principles for Visual Analytics in Operations Contexts

Cited by 5 publications

References 11 publications

A Workflow to Systematically Design Uncertainty-Aware Visual Analytics Applications

A Workflow to Systematically Design Uncertainty-Aware Visual Analytics Applications

A workflow to systematically design uncertainty-aware visual analytics applications

JPL/Caltech ArtCenter

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