Personal Visualization and Personal Visual Analytics

Description: 

Abstract—Data surrounds each and every one of us in our daily lives, ranging from exercise logs, to archives of our interactions with others
on social media, to online resources pertaining to our hobbies. There is enormous potential for us to use these data to understand ourselves
better and make positive changes in our lives. Visualization (Vis) and Visual Analytics (VA) offer substantial opportunities to help individuals
gain insights about themselves, their communities and their interests; however, designing tools to support data analysis in non-professional
life brings a unique set of research and design challenges. We investigate the requirements and research directions required to take full
advantage of Vis and VA in a personal context. We develop a taxonomy of design dimensions to provide a coherent vocabulary for
discussing Personal Visualization and Personal Visual Analytics. By identifying and exploring clusters in the design space, we discuss
challenges and share perspectives on future research. This work brings together research that was previously scattered across disciplines.
Our goal is to call research attention to this space and engage researchers to explore the enabling techniques and technology that will
support people to better understand data relevant to their personal lives, interests, and needs.

Authors: 
Dandan Huang, Melanie Tory, Bon Adriel Aseniero, Lyn Bartram, Scott Bateman, Sheelagh Carpendale, Anthony Tang, and Robert Woodbury
Publication Date: 
Sunday, March 1, 2015
Publication Information: 
IEEE Transactions on Visualization and Computer Graphics, vol. 21, no. 3, pp. 420 - 433, Mar. 2015