TY - GEN
T1 - Grand Challenges in Immersive Analytics
AU - Ens, Barrett
AU - Bach, Benjamin
AU - Cordeil, Maxime
AU - Engelke, Ulrich
AU - Serrano, Marcos
AU - Willett, Wesley
AU - Prouzeau, Arnaud
AU - Anthes, Christoph
AU - Büschel, Wolfgang
AU - Dunne, Cody
AU - Dwyer, Tim
AU - Grubert, Jens
AU - Haga, Jason H.
AU - Kirshenbaum, Nurit
AU - Kobayashi, Dylan
AU - Lin, Tica
AU - Olaosebikan, Monsurat
AU - Pointecker, Fabian
AU - Saffo, David
AU - Saquib, Nazmus
AU - Schmalstieg, Dieter
AU - Szafir, Danielle Albers
AU - Whitlock, Matt
AU - Yang, Yalong
N1 - DBLP's bibliographic metadata records provided through http://dblp.org/search/publ/api are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.
PY - 2021/5/6
Y1 - 2021/5/6
N2 - Immersive Analytics is a quickly evolving field that unites several areas such as visualisation, immersive environments, and humancomputer interaction to support human data analysis with emerging technologies. This research has thrived over the past years with multiple workshops, seminars, and a growing body of publications, spanning several conferences. Given the rapid advancement of interaction technologies and novel application domains, this paper aims toward a broader research agenda to enable widespread adoption. We present 17 key research challenges developed over multiple sessions by a diverse group of 24 international experts, initiated from a virtual scientific workshop at ACM CHI 2020. These challenges aim to coordinate future work by providing a systematic roadmap of current directions and impending hurdles to facilitate productive and effective applications for Immersive Analytics. Immersive analytics, grand research challenges, data visualisation, augmented reality, virtual reality.
AB - Immersive Analytics is a quickly evolving field that unites several areas such as visualisation, immersive environments, and humancomputer interaction to support human data analysis with emerging technologies. This research has thrived over the past years with multiple workshops, seminars, and a growing body of publications, spanning several conferences. Given the rapid advancement of interaction technologies and novel application domains, this paper aims toward a broader research agenda to enable widespread adoption. We present 17 key research challenges developed over multiple sessions by a diverse group of 24 international experts, initiated from a virtual scientific workshop at ACM CHI 2020. These challenges aim to coordinate future work by providing a systematic roadmap of current directions and impending hurdles to facilitate productive and effective applications for Immersive Analytics. Immersive analytics, grand research challenges, data visualisation, augmented reality, virtual reality.
KW - Augmented reality
KW - Data visualisation
KW - Grand research challenges
KW - Immersive analytics
KW - Virtual reality
UR - http://www.scopus.com/inward/record.url?scp=85104088576&partnerID=8YFLogxK
U2 - 10.1145/3411764.3446866
DO - 10.1145/3411764.3446866
M3 - Conference paper
T3 - Conference on Human Factors in Computing Systems - Proceedings
SP - 459:1-459:17
BT - CHI 2021 - Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
PB - Association of Computing Machinery
T2 - 2021 CHI Conference on Human Factors in Computing Systems: Making Waves, Combining Strengths
Y2 - 8 May 2021 through 13 May 2021
ER -