TY - JOUR
T1 - AxiSketcher
T2 - Interactive Nonlinear Axis Mapping of Visualizations through User Drawings
AU - Kwon, Bum Chul
AU - Kim, Hannah
AU - Wall, Emily
AU - Choo, Jaegul
AU - Park, Haesun
AU - Endert, Alex
N1 - Funding Information:
Support for the research is partially provided by the DHS VACCINE Center of Excellence, by the framework of international cooperation program managed by National Research Foundation of Korea (NRF- 2015K2A1A2070536), and by DARPA XDATA grant (FA8750-12-2- 0309) as well as NSF grant (CCF-0808863).
Publisher Copyright:
© 2016 IEEE.
PY - 2017/1
Y1 - 2017/1
N2 - Visual analytics techniques help users explore high-dimensional data. However, it is often challenging for users to express their domain knowledge in order to steer the underlying data model, especially when they have little attribute-level knowledge. Furthermore, users' complex, high-level domain knowledge, compared to low-level attributes, posits even greater challenges. To overcome these challenges, we introduce a technique to interpret a user's drawings with an interactive, nonlinear axis mapping approach called AxiSketcher. This technique enables users to impose their domain knowledge on a visualization by allowing interaction with data entries rather than with data attributes. The proposed interaction is performed through directly sketching lines over the visualization. Using this technique, users can draw lines over selected data points, and the system forms the axes that represent a nonlinear, weighted combination of multidimensional attributes. In this paper, we describe our techniques in three areas: 1) the design space of sketching methods for eliciting users' nonlinear domain knowledge; 2) the underlying model that translates users' input, extracts patterns behind the selected data points, and results in nonlinear axes reflecting users' complex intent; and 3) the interactive visualization for viewing, assessing, and reconstructing the newly formed, nonlinear axes.
AB - Visual analytics techniques help users explore high-dimensional data. However, it is often challenging for users to express their domain knowledge in order to steer the underlying data model, especially when they have little attribute-level knowledge. Furthermore, users' complex, high-level domain knowledge, compared to low-level attributes, posits even greater challenges. To overcome these challenges, we introduce a technique to interpret a user's drawings with an interactive, nonlinear axis mapping approach called AxiSketcher. This technique enables users to impose their domain knowledge on a visualization by allowing interaction with data entries rather than with data attributes. The proposed interaction is performed through directly sketching lines over the visualization. Using this technique, users can draw lines over selected data points, and the system forms the axes that represent a nonlinear, weighted combination of multidimensional attributes. In this paper, we describe our techniques in three areas: 1) the design space of sketching methods for eliciting users' nonlinear domain knowledge; 2) the underlying model that translates users' input, extracts patterns behind the selected data points, and results in nonlinear axes reflecting users' complex intent; and 3) the interactive visualization for viewing, assessing, and reconstructing the newly formed, nonlinear axes.
KW - axis mapping
KW - axis visualization
KW - human-centered visual analytics
KW - interactive model steering
KW - sketch
UR - http://www.scopus.com/inward/record.url?scp=84999115025&partnerID=8YFLogxK
U2 - 10.1109/TVCG.2016.2598446
DO - 10.1109/TVCG.2016.2598446
M3 - Article
C2 - 27514048
AN - SCOPUS:84999115025
SN - 1077-2626
VL - 23
SP - 221
EP - 230
JO - IEEE Transactions on Visualization and Computer Graphics
JF - IEEE Transactions on Visualization and Computer Graphics
IS - 1
M1 - 7534876
ER -