AxiSketcher : Interactive Nonlinear Axis Mapping of Visualizations through User Drawings

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 dom...

Ausführliche Beschreibung

Bibliographische Detailangaben
Veröffentlicht in:IEEE transactions on visualization and computer graphics. - 1996. - 23(2017), 1 vom: 11. Jan., Seite 221-230
1. Verfasser: Kwon, Bum Chul (VerfasserIn)
Weitere Verfasser: Kim, Hannah, Wall, Emily, Choo, Jaegul, Park, Haesun, Endert, Alex
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2017
Zugriff auf das übergeordnete Werk:IEEE transactions on visualization and computer graphics
Schlagworte:Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S.
LEADER 01000naa a22002652 4500
001 NLM263334643
003 DE-627
005 20231224203657.0
007 cr uuu---uuuuu
008 231224s2017 xx |||||o 00| ||eng c
028 5 2 |a pubmed24n0877.xml 
035 |a (DE-627)NLM263334643 
035 |a (NLM)27514048 
040 |a DE-627  |b ger  |c DE-627  |e rakwb 
041 |a eng 
100 1 |a Kwon, Bum Chul  |e verfasserin  |4 aut 
245 1 0 |a AxiSketcher  |b Interactive Nonlinear Axis Mapping of Visualizations through User Drawings 
264 1 |c 2017 
336 |a Text  |b txt  |2 rdacontent 
337 |a ƒaComputermedien  |b c  |2 rdamedia 
338 |a ƒa Online-Ressource  |b cr  |2 rdacarrier 
500 |a Date Completed 30.07.2018 
500 |a Date Revised 30.07.2018 
500 |a published: Print-Electronic 
500 |a Citation Status PubMed-not-MEDLINE 
520 |a 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 
650 4 |a Journal Article 
650 4 |a Research Support, Non-U.S. Gov't 
650 4 |a Research Support, U.S. Gov't, Non-P.H.S. 
700 1 |a Kim, Hannah  |e verfasserin  |4 aut 
700 1 |a Wall, Emily  |e verfasserin  |4 aut 
700 1 |a Choo, Jaegul  |e verfasserin  |4 aut 
700 1 |a Park, Haesun  |e verfasserin  |4 aut 
700 1 |a Endert, Alex  |e verfasserin  |4 aut 
773 0 8 |i Enthalten in  |t IEEE transactions on visualization and computer graphics  |d 1996  |g 23(2017), 1 vom: 11. Jan., Seite 221-230  |w (DE-627)NLM098269445  |x 1941-0506  |7 nnns 
773 1 8 |g volume:23  |g year:2017  |g number:1  |g day:11  |g month:01  |g pages:221-230 
912 |a GBV_USEFLAG_A 
912 |a SYSFLAG_A 
912 |a GBV_NLM 
912 |a GBV_ILN_350 
951 |a AR 
952 |d 23  |j 2017  |e 1  |b 11  |c 01  |h 221-230