Details
| Originalsprache | Englisch |
|---|---|
| Seitenumfang | 4 |
| Publikationsstatus | Veröffentlicht - 2017 |
Abstract
and relationships in data. However, the exploration may start without
a clear conception about what attributes to pick or what visualizations to choose in order to develop an understanding of the data. In
this work we aim to support the exploration process by automatically
choosing attributes according to an information-theoretic measure
and by providing a simple means of navigation through the space of
visualizations. The system suggests data attributes to be visualized
and the visualization’s type and appearance. The user intuitively
modifies these suggestions by performing swiping gestures on a
tablet device. Attribute suggestions are based on the mutual information between multiple random variables (MMI). The results of
a preliminary user study (N = 12 participants) show the applicability of MMI for guided exploratory data analysis and confirm the
system’s general usability (SUS score: 74).
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4 S. 2017.
Publikation: Sonstige Publikation › Forschung
}
TY - GEN
T1 - Immersive Navigation in Visualization Spaces through Swipe Gestures and Optimal Attribute Selection
AU - Kassel, Jan Frederik
AU - Rohs, Michael
PY - 2017
Y1 - 2017
N2 - Exploratory data analysis is an essential step in discovering patternsand relationships in data. However, the exploration may start withouta clear conception about what attributes to pick or what visualizations to choose in order to develop an understanding of the data. Inthis work we aim to support the exploration process by automaticallychoosing attributes according to an information-theoretic measureand by providing a simple means of navigation through the space ofvisualizations. The system suggests data attributes to be visualizedand the visualization’s type and appearance. The user intuitivelymodifies these suggestions by performing swiping gestures on atablet device. Attribute suggestions are based on the mutual information between multiple random variables (MMI). The results ofa preliminary user study (N = 12 participants) show the applicability of MMI for guided exploratory data analysis and confirm thesystem’s general usability (SUS score: 74).
AB - Exploratory data analysis is an essential step in discovering patternsand relationships in data. However, the exploration may start withouta clear conception about what attributes to pick or what visualizations to choose in order to develop an understanding of the data. Inthis work we aim to support the exploration process by automaticallychoosing attributes according to an information-theoretic measureand by providing a simple means of navigation through the space ofvisualizations. The system suggests data attributes to be visualizedand the visualization’s type and appearance. The user intuitivelymodifies these suggestions by performing swiping gestures on atablet device. Attribute suggestions are based on the mutual information between multiple random variables (MMI). The results ofa preliminary user study (N = 12 participants) show the applicability of MMI for guided exploratory data analysis and confirm thesystem’s general usability (SUS score: 74).
M3 - Other publication
ER -