TY - GEN
T1 - GraphScape
T2 - 2017 ACM SIGCHI Conference on Human Factors in Computing Systems, CHI 2017
AU - Kim, Younghoon
AU - Wongsuphasawat, Kanit
AU - Hullman, Jessica
AU - Heer, Jeffrey
N1 - Publisher Copyright:
© 2017 ACM.
PY - 2017/5/2
Y1 - 2017/5/2
N2 - We present GraphScape, a directed graph model of the visualization design space that supports automated reasoning about visualization similarity and sequencing. Graph nodes represent grammar-based chart specifications and edges represent edits that transform one chart to another. We weight edges with an estimated cost of the difficulty of interpreting a target visualization given a source visualization. We contribute (1) a method for deriving transition costs via a partial ordering of edit operations and the solution of a resulting linear program, and (2) a global weighting term that rewards consistency across transition subsequences. In a controlled experiment, subjects rated visualization sequences covering a taxonomy of common transition types. In all but one case, GraphScape's highest-ranked suggestion aligns with subjects' top-rated sequences. Finally, we demonstrate applications of GraphScape to automatically sequence visualization presentations, elaborate transition paths between visualizations, and recommend design alternatives (e.g., to improve scalability while minimizing design changes).
AB - We present GraphScape, a directed graph model of the visualization design space that supports automated reasoning about visualization similarity and sequencing. Graph nodes represent grammar-based chart specifications and edges represent edits that transform one chart to another. We weight edges with an estimated cost of the difficulty of interpreting a target visualization given a source visualization. We contribute (1) a method for deriving transition costs via a partial ordering of edit operations and the solution of a resulting linear program, and (2) a global weighting term that rewards consistency across transition subsequences. In a controlled experiment, subjects rated visualization sequences covering a taxonomy of common transition types. In all but one case, GraphScape's highest-ranked suggestion aligns with subjects' top-rated sequences. Finally, we demonstrate applications of GraphScape to automatically sequence visualization presentations, elaborate transition paths between visualizations, and recommend design alternatives (e.g., to improve scalability while minimizing design changes).
KW - Automated design
KW - Model
KW - Sequence
KW - Transition
KW - Visualization
UR - http://www.scopus.com/inward/record.url?scp=85022181907&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85022181907&partnerID=8YFLogxK
U2 - 10.1145/3025453.3025866
DO - 10.1145/3025453.3025866
M3 - Conference contribution
AN - SCOPUS:85022181907
T3 - Conference on Human Factors in Computing Systems - Proceedings
SP - 2628
EP - 2638
BT - CHI 2017 - Proceedings of the 2017 ACM SIGCHI Conference on Human Factors in Computing Systems
PB - Association for Computing Machinery
Y2 - 6 May 2017 through 11 May 2017
ER -