Personal Visualization and Personal Visual Analytics

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dc.contributor.authorDandan, Huang
dc.contributor.authorTory, Melanie
dc.contributor.authorAseniero, Bon Adriel
dc.contributor.authorBartram, Lyn
dc.contributor.authorBateman, Scott
dc.contributor.authorCarpendale, Sheelagh
dc.contributor.authorTang, Anthony
dc.contributor.authorWoodbury, Robert
dc.date.accessioned2015-07-29T19:33:01Z
dc.date.available2015-07-29T19:33:01Z
dc.date.issued2015
dc.description.abstractData 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.en_US
dc.description.refereedYesen_US
dc.identifier.doi10.1109/TVCG.2014.2359887
dc.identifier.doihttp://dx.doi.org/10.11575/PRISM/35585
dc.identifier.urihttp://hdl.handle.net/1880/50707
dc.publisherIEEEen_US
dc.publisher.urlhttp://doi.ieeecomputersociety.org/10.1109/TVCG.2014.2359887en_US
dc.titlePersonal Visualization and Personal Visual Analyticsen_US
dc.typeunknown
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