Visualization beyond the Desktop--the Next Big Thing

Roberts, Jonathan C.; Ritsos, Panagiotis D.; Badam, Sriram Karthik; Brodbeck, Dominique; Kennedy, Jessie; Elmqvist, Niklas

doi:10.1109/mcg.2014.82

Cited by 106 publications

(63 citation statements)

References 18 publications

(14 reference statements)

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“…Our work is an extension of Andromeda [50] for visual analytics beyond the desktop [51]. The desktop-based application has several educational limitations for learners who have not experienced data analytics yet.…”

Section: Teach High Dimensional Data Using Visualizationmentioning

confidence: 99%

“…In our everyday life, we are heavily accustomed to processing information integrated from vision, hearing, touching, moving, and etc. It is desirable to exploit the same approach to interact with data [51]. Here, we took a more proactive approach to employ our own bodies as movable data points so that we are able to map data analysis to an integrated sensory/motor activity, not just to visual.…”

Section: Teach High Dimensional Data Using Visualizationmentioning

confidence: 99%

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Be the Data: Embodied Visual Analytics

Chen¹,

Self

House

et al. 2018

IEEE Trans. Learning Technol.

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(ABSTRACT)With the rise of big data, it is becoming increasingly important to educate students about data analytics. In particular, students without a strong mathematical background usually have an unenthusiastic attitude towards high-dimensional data and find it challenging to understand relevant complex analytical methods, such as dimension reduction. In this thesis, we present an embodied approach for visual analytics designed to teach students exploring alternative 2D projections of high dimensional data points using weighted multidimensional scaling. We proposed a novel concept, Be the Data, and its application to explore the possibilities of using human's embodied resources to learn from high dimensional data. In our system, each student embodies a data point and the position of students in a physical space represents a 2D projection of the high-dimensional data.Students physically moves in a room with respect to others to interact with alternative projections and receive visual feedback. We conducted educational workshops with students inexperienced in relevant data analytical methods. Our findings indicate that the students were able to learn about high-dimensional data and data analysis process despite their low level of knowledge about the complex analytical methods. Similarly, we applied Be the Data to social meetings. We used the same techniques to analyze and display social-cluster related information to facilitate social interactions in real time. Participants information 323.2 A quantitative summary of students' understanding about the key concepts (i.e., variable, relative distance, dimension reduction, data exploration), interests, and confidence towards learning high-dimensional data before and after the workshop. DR stands for dimension reduction. Column 3 and 4 are observed proportion of correct answers for the pre and post surveys. Column 5 and 6 are the expected difference and the credible interval for the difference in proportions. Column 7 are the p-values from a two-tailed two sample t-test. The * in column 6 and 7 flags questions when there is important difference in pre and post. 373.3 A summary of students' embodied interaction during the activity 48 4.1 A portion of the high-dimensional dataset that describes participants in one social meeting.

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Section: Teach High Dimensional Data Using Visualizationmentioning

confidence: 99%

Section: Teach High Dimensional Data Using Visualizationmentioning

confidence: 99%

Be the Data: Embodied Visual Analytics

Chen¹,

Self

House

et al. 2018

IEEE Trans. Learning Technol.

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“…Visualization has been developing since Ivan Sutherland had introduced sketchpad (robot draftsman) in 1963 to show that computer graphics could be used for both artistic and technical purpose [34].…”

Section: Visualizationmentioning

confidence: 99%

“…Visualization algorithms have been designed by engineers and researchers to map the data into a visual form or structure; bar charts, scatter plots, line graphs and many more [34]. In [40] found that most of visualization sources in scientific publications are diagrams, graphs, charts, plots, flowcharts, timelines and tables.…”

Section: Visualizationmentioning

confidence: 99%

Classification of visualization exudates fundus images results using support vector machine

Hassan¹,

Tahir²,

Zabidi³

et al. 2018

J. Fundam and Appl Sci.

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This paper classifies the characteristics of normal and exudates fundus images by determine its accuracy for diagnostic purposes.images (81 normal and 68 exudates) from MESSIDOR databas the fundus images. The OD removed fundus image and fundus image with the exudates areas removed. The SVM1 classifier was applied to 30 test fundus images to determine the best optimal parameter. The kernel function settings an effect on the classification results. For SVM1, the best parameter in classifying pixels is linear kernel function. The visualization results using CAC and radar chart are classified using SVM2 to determine its accuracy. pixels in fundus image using linear kernel function of SVM1 to diagnose DR. an effect on the classification results. For SVM1, the best parameter in classifying pixels is linear kernel function. The visualization results using CAC and radar chart are classified using has proven to discriminated exudates and non exudates pixels in fundus image using linear kernel function of SVM1 to diagnose DR.Diabetic retinopathy (DR); Optic disc (OD); Support Vector Machine (SVM);

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“…Roberts and Walker [2010] encouraged the InfoVis community to develop a unified theory that covers all human senses and allows the integration of multiple modalities. They further foresee visualizations beyond the desktop, which use different modalities to both perceive and interact with information as "the next big thing" [Roberts et al, 2014]. Data representa- Figure 4.14: Examples of physicalizations using multiple modalities.…”

Section: Examples For Physicalizationsmentioning

confidence: 99%

Exploring the Potential of Physical Visualizations

Stusak¹

2015

Proceedings of the Ninth International Conference on Tangible, Embedded, and Embodied Interaction

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The goal of an external representation of abstract data is to provide insights and convey information about the structure of the underlying data, therefore helping people execute tasks and solve problems more effectively. Apart from the popular and well-studied digital visualization of abstract data there are other scarcely studied perceptual channels to represent data such as taste, sound or haptic. My thesis focuses on the latter and explores in which ways human knowledge and ability to sense and interact with the physical non-digital world can be used to enhance the way in which people analyze and explore abstract data. Emerging technological progress in digital fabrication allow an easy, fast and inexpensive production of physical objects. Machines such as laser cutters and 3D printers enable an accurate fabrication of physical visualizations with different form factors as well as materials. This creates, for the first time, the opportunity to study the potential of physical visualizations in a broad range.The thesis starts with the description of six prototypes of physical visualizations from static examples to digitally augmented variations to interactive artifacts. Based on these explorations, three promising areas of potential for physical visualizations were identified and investigated in more detail: perception & memorability, communication & collaboration, and motivation & self-reflection.The results of two studies in the area of information recall showed that participants who used a physical bar chart retained more information compared to the digital counterpart. Particularly facts about maximum and minimum values were be remembered more efficiently, when they were perceived from a physical visualization.Two explorative studies dealt with the potential of physical visualizations regarding communication and collaboration. The observations revealed the importance on the design and aesthetic of physical visualizations and indicated a great potential for their utilization by audiences with less interest in technology. The results also exposed the current limitations of physical visualizations, especially in contrast to their well-researched digital counterparts.In the area of motivation we present the design and evaluation of the Activity Sculptures project. We conducted a field study, in which we investigated physical visualizations of personal running activity. It was discovered that these sculptures generated curiosity and experimentation regarding the personal running behavior as well as evoked social dynamics such as discussions and competition.Based on the findings of the aforementioned studies this thesis concludes with two theoretical contributions on the design and potential of physical visualizations. On the one hand, it proposes a conceptual framework for material representations of personal data by describing a production and consumption lens. The goal is to encourage artists and designers working in the field of personal informatics to harness the interactive capabilities af...

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Visualization beyond the Desktop--the Next Big Thing

Cited by 106 publications

References 18 publications

Be the Data: Embodied Visual Analytics

Be the Data: Embodied Visual Analytics

Classification of visualization exudates fundus images results using support vector machine

Exploring the Potential of Physical Visualizations

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