Shixia Liu, Michelle X. Zhou, et al.
ACM TIST
We are developing an automated visualization system that helps users combine two or more existing information graphics to form an integrated view. To establish empirical foundations for building such a system, we designed and conducted two studies on Amazon Mechanical Turk to understand users' comprehension and preferences of composite visualization under different conditions (e.g., data and tasks). In Study 1, we collected more than 1,500 textual descriptions capturing about 500 participants' insights of given information graphics, which resulted in a task-oriented taxonomy of visual insights. In Study 2, we asked 240 participants to rank composite visualizations by their suitability for acquiring a given visual insight identified in Study 1, which resulted in ranked user preferences of visual compositions for acquiring each type of insight. In this article, we report the details of our two studies and discuss the broader implications of our crowdsourced research methodology and results to HCI-driven visualization research.© 2014 ACM.
Shixia Liu, Michelle X. Zhou, et al.
ACM TIST
Dorit Nuzman, David Maze, et al.
SYSTOR 2011
Jia Cui, Yonggang Deng, et al.
ASRU 2009
Jonathan Malkin, Xiao Li, et al.
Computer Speech and Language