Perspectives of visualization onboarding and guidance in VA

Stoiber, Christina; Ceneda, Davide; Wagner, Markus; Schetinger, Victor; Gschwandtner, Theresia; Streit, Marc; Miksch, Silvia; Aigner, Wolfgang

doi:10.1016/j.visinf.2022.02.005

Cited by 20 publications

(13 citation statements)

References 42 publications

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“…4,15,16 Furthermore, no matter how knowledgeable a VA expert is, they might still be subject to various cognitive biases in their analyses -especially subconscious ones 17 -and guidance can also help to combat these biases through revealing them and providing suggestions when indicators for bias are detected. 18 Other research has also discussed guidance goals, such as attention management by guiding users to views that are currently important, 19 supporting user learning and training with onboarding guidance that walks users through visualization elements, 20 engaging users by prompting them when they are inactive, 21 as well as aiding the verification of analyses through monitoring of analytical hypotheses. 15 Motivation.…”

Section: Generic Guidance Beyond Knowledge Gapsmentioning

confidence: 99%

Providing visual analytics guidance through decision support

Han

Schulz

2023

Information Visualization

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Guidance in visual analytics aims to support users in accomplishing their analytical goals and generating insights. Different approaches for guidance are widely adopted in many tools and frameworks for various purposes – from helping to focus on relevant data subspaces to selecting suitable visualization techniques. With each of these different purposes come specific considerations on how to provide the needed guidance. In this paper, we propose a generic method for making these considerations by framing the guidance problem as a decision problem and applying decision making theory and models toward its solution. This method passes through three stages: (1) identifying decision points; (2) deriving and evaluating alternatives; (3) visualizing the resulting alternatives to support users in comparing them and making their choice. Our method is realized as a set of practical worksheets and illustrated by applying it to a use case of providing guidance among different clustering methods. Finally, we compare our method with existing guidance frameworks to relate and delineate the respective goals and contributions of each.

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Section: Generic Guidance Beyond Knowledge Gapsmentioning

confidence: 99%

Providing visual analytics guidance through decision support

Han

Schulz

2023

Information Visualization

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“…Many conceptual models give high-level blueprints. Stoiber et al [62] classified these models as (1) descriptive and (2) mathematical. For (1), the knowledge generation model proposed by Sacha et al [57] is representative.…”

Section: Knowledge-assisted Visual Analyticsmentioning

confidence: 99%

Incorporation of Human Knowledge Into Data Embeddings to Improve Pattern Significance and Interpretability

Zhou

2022

IEEE Trans. Visual. Comput. Graphics

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2 a1 b1 c1 b2 a2 c1 c2 Fig. 1. The visualization system interface that consists of three components: (a) Knowledge Editor (KE), (b) Data Projector (DP), and (c) Pattern Explainer (PE). The analyst is analyzing the Covid-19 dataset [2] that consists of 173 sequences, each recording the number of monthly-confirmed cases (per million people) from May 2020 to April 2021 of a country. The analyst creates six country groups (see colored tree nodes) according to their knowledge using (a) and incorporates the class information into the embeddings of countries to generate the projection with clear and class-consistent visual structures that foreshadow underlying data patterns. (b2)The embedding projection without knowledge where does not exist visual structure.

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“…Visualization onboarding is the process of supporting users in reading, interpreting, and extracting information from visual representations of data [50]. Stoiber et al [49] found that a VA system may lack low-level information about the data, such as understanding principles of the specific data format, data types, or data structure, which limits data selection and manipulation [22]. Vaishali et al [12] found that non-expert users often lack visualization literacy to interpret the data and understand the interactions with and between visualizations in a dashboard.…”

Section: Onboarding In Visual Analyticsmentioning

confidence: 99%

“…To address these problems, researchers focus on enhancing VA through visualization onboarding [49], interaction recommendation and guidance [4], [29], [47], and result summarization [8], [44].…”

Section: Introductionmentioning

confidence: 99%

Hyperbolic Personalized Tag Recommendation

Zhao

Zhang

Shang

et al. 2022

Lecture Notes in Computer Science

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Visual analytics supports data analysis tasks within complex domain problems. However, due to the richness of data types, visual designs, and interaction designs, users need to recall and process a significant amount of information when they visually analyze data. These challenges emphasize the need for more intelligent visual analytics methods. Large language models have demonstrated the ability to interpret various forms of textual data, offering the potential to facilitate intelligent support for visual analytics. We propose LEVA, a framework that uses large language models to enhance users' VA workflows at multiple stages: onboarding, exploration, and summarization. To support onboarding, we use large language models to interpret visualization designs and view relationships based on system specifications. For exploration, we use large language models to recommend insights based on the analysis of system status and data to facilitate mixed-initiative exploration. For summarization, we present a selective reporting strategy to retrace analysis history through a stream visualization and generate insight reports with the help of large language models. We demonstrate how LEVA can be integrated into existing visual analytics systems. Two usage scenarios and a user study suggest that LEVA effectively aids users in conducting visual analytics.

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Perspectives of visualization onboarding and guidance in VA

Cited by 20 publications

References 42 publications

Providing visual analytics guidance through decision support

Providing visual analytics guidance through decision support

Incorporation of Human Knowledge Into Data Embeddings to Improve Pattern Significance and Interpretability

Hyperbolic Personalized Tag Recommendation

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