Characterizing the Quality of Insight by Interactions: A Case Study

He, Chen; Micallef, Luana; He, Liye; Gopalacharyulu, Peddinti; Aittokallio, Tero

doi:10.1109/tvcg.2020.2977634

Cited by 10 publications

(14 citation statements)

References 68 publications

(110 reference statements)

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“…Following the above example, visualizations could recommend other diverse insights if the user constantly refer to vertical reference lines. A comparison with previous discoveries (e.g., [15,26]) revealed that interactions / entity references could infer insight characteristics, but the particular entity and insight relations could be relevant to the type of dataset and visualization being studied. Nonetheless, the approach we used to explore the RQ is applicable to study other visualizations, as explained in Sect.…”

Section: Introductionmentioning

confidence: 88%

“…Through correlating interaction types and insight characteristics, prior work revealed the tight connection between interaction and insight. For instance, studies showed that exploration actions promoted the number of insights [24] and could lead to unexpected discoveries about the data [26]. These findings imply the potential of automatically characterizing insight using interactions, alleviating user efforts in tagging insights [56] while facilitating insight management and recommendation [13].…”

Section: Introductionmentioning

confidence: 99%

“…Guo et al [24] correlated the number of interactions and insights of users using a visualization tool and discovered that exploration actions promoted insight generation whereas filtering actions inhibited it. Further, He et al [26] analyzed an insight from multiple perspectives and correlated the scale of insight characteristics to the number of interactions by studying a domain-related visualization. They found evidence that exploration actions related to unexpected insight, and the pattern of drilling down into data of interest led to insight with high domain values.…”

Section: Introductionmentioning

confidence: 99%

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Characterizing Visualization Insights through Entity-Based Interaction: An Exploratory Study

He¹,

Vuong²

2022

Preprint

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One of the primary purposes of visualization is to assist users in discovering insights. While there has been much research in information visualization aiming at complex data transformation and novel presentation techniques, relatively little has been done to understand how users derive insights through interactive visualization of data. This paper presents a crowdsourced study with 158 participants investigating the relation between entity-based interaction (an action + its target entity) and the resulting insight. To this end, we generalized the interaction with an existing CO 2 Explorer as entity-based interaction and enabled users to input notes and refer to relevant entities to assist their narratives. We logged interactions of users freely exploring the visualization and characterized their externalized insights about the data. Using entity-based interactions and references to infer insight characteristics (category, overview versus detail, and prior knowledge), we found evidence that compared with interactions, entity references improved insight characterization from slight/fair to fair/moderate agreements. To interpret prediction outcomes, feature importance and correlation analysis indicated that, e.g., detailed insights tended to have more mouse-overs in the chart area and cite the vertical reference lines in the line chart as evidence. We discuss study limitations and implications on knowledge-assisted visualization, e.g., insight recommendations based on user exploration.

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Section: Introductionmentioning

confidence: 88%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Characterizing Visualization Insights through Entity-Based Interaction: An Exploratory Study

He¹,

Vuong²

2022

Preprint

Self Cite

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“…Interaction with content positively influences its conception (He et al, 2021;Pike et al, 2009) and helps to explore concepts. Having established the mathematical model, we developed the Alvin simulation software to support the conception and exploration of the gas exchange process in a single alveolus.…”

Section: Visualization and Interactivity: The Alvin Applicationmentioning

confidence: 99%

Interactive, visual simulation of a spatio-temporal model of gas exchange in the human alveolus

Schmid

Knote

Mueck

et al. 2021

Preprint

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In interdisciplinary fields such as systems biology, close collaboration between experimentalists and theorists is crucial for the success of a project. Theoretical modeling in physiology usually describes complex systems with many interdependencies. On one hand, these models have to be grounded on experimental data. On the other hand, experimenters must be able to penetrate the model in its dependencies in order to correctly interpret the results in the physiological context. When theorists and experimenters collaborate, communicating results and ideas is sometimes challenging. We promote interactive, visual simulations as an engaging way to communicate theoretical models in physiology and to thereby advance our understanding of the process of interest. We defined a new spatio-temporal model for gas exchange in the human alveolus and implemented it in an interactive simulation software named Alvin. In Alvin, the course of the simulation can be traced in a three-dimensional rendering of an alveolus and dynamic plots. The user can interact by configuring essential model parameters. Alvin allows to run and compare multiple simulation instances simultaneously. The mathematical model was developed with the aim of visualization and the simulation software was engineered based on a requirements analysis. Our work resulted in an integrative gas exchange model and an interactive application that exceed the current standards. We exemplified the use of Alvin for research by identifying unknown dependencies in published experimental data. Employing a detailed questionnaire, we showed the benefits of Alvin for education. We postulate that interactive, visual simulation of theoretical models, as we have implemented with Alvin on respiratory processes in the alveolus, can be of great help for communication between specialists and thereby advancing research.

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“…Situating data as one of many, but limited, perspectives of reality, we introduce personal knowledge as another perspective that deserves representation in visualizations. Personal knowledge about data can influence the interpretation of existing data [39,40], shape how knowledge is produced [41,42,43,44], and affect how decisions are made [45,46,16]. In this section, we frame this personal knowledge formally as data hunches, arguing for their potential to produce richer depictions of reality, making connections and distinctions between data hunches and existing concepts surrounding data, and providing a characterization about the common types of data hunches.…”

Section: Data Hunchesmentioning

confidence: 99%

Data Hunches: Incorporating Personal Knowledge into Visualizations

Lin¹,

Akbaba²,

Meyer³

et al. 2021

Preprint

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The trouble with data is that often it provides only an imperfect representation of the phenomenon of interest. When reading and interpreting data, personal knowledge about the data plays an important role. Data visualization, however, has neither a concept defining personal knowledge about datasets, nor the methods or tools to robustly integrate them into an analysis process, thus hampering analysts' ability to express their personal knowledge about datasets, and others to learn from such knowledge. In this work, we define such personal knowledge about datasets as data hunches and elevate this knowledge to another form of data that can be externalized, visualized, and used for collaboration. We establish the implications of data hunches and provide a design space for externalizing and communicating data hunches through visualization techniques. We envision such a design space will empower users to externalize their personal knowledge and support the ability to learn from others' data hunches.

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Characterizing the Quality of Insight by Interactions: A Case Study

Cited by 10 publications

References 68 publications

Characterizing Visualization Insights through Entity-Based Interaction: An Exploratory Study

Characterizing Visualization Insights through Entity-Based Interaction: An Exploratory Study

Interactive, visual simulation of a spatio-temporal model of gas exchange in the human alveolus

Data Hunches: Incorporating Personal Knowledge into Visualizations

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