TY - GEN
T1 - Toward a multi-analyst, collaborative framework for visual analytics
AU - Brennan, Susan E.
AU - Mueller, Klaus
AU - Zelinsky, Greg
AU - Ramakrishnan, I. V.
AU - Warren, David S.
AU - Kaufman, Arie
PY - 2006
Y1 - 2006
N2 - We describe a framework for the display of complex, multidimensional data, designed to facilitate exploration, analysis, and collaboration among multiple analysts. This framework aims to support human collaboration by making it easier to share representations, to translate from one point of view to another, to explain arguments, to update conclusions when underlying assumptions change, and to justify or account for decisions or actions. Multidimensional visualization techniques are used with interactive, context-sensitive, and tunable graphs. Visual representations are flexibly generated using a knowledge representation scheme based on annotated logic; this enables not only tracking and fusing different viewpoints, but also unpacking them. Fusing representations supports the creation of multidimensional meta-displays as well as the translation or mapping from one point of view to another. At the same time, analysts also need to be able to unpack one another's complex chains of reasoning, especially if they have reached different conclusions, and to determine the implications, if any, when underlying assumptions or evidence turn out to be false. The framework enables us to support a variety of scenarios as well as to systematically generate and test experimental hypotheses about the impact of different kinds of visual representations upon interactive collaboration by teams of distributed analysts.
AB - We describe a framework for the display of complex, multidimensional data, designed to facilitate exploration, analysis, and collaboration among multiple analysts. This framework aims to support human collaboration by making it easier to share representations, to translate from one point of view to another, to explain arguments, to update conclusions when underlying assumptions change, and to justify or account for decisions or actions. Multidimensional visualization techniques are used with interactive, context-sensitive, and tunable graphs. Visual representations are flexibly generated using a knowledge representation scheme based on annotated logic; this enables not only tracking and fusing different viewpoints, but also unpacking them. Fusing representations supports the creation of multidimensional meta-displays as well as the translation or mapping from one point of view to another. At the same time, analysts also need to be able to unpack one another's complex chains of reasoning, especially if they have reached different conclusions, and to determine the implications, if any, when underlying assumptions or evidence turn out to be false. The framework enables us to support a variety of scenarios as well as to systematically generate and test experimental hypotheses about the impact of different kinds of visual representations upon interactive collaboration by teams of distributed analysts.
KW - Collaborative and distributed visualization
KW - Data management and knowledge representation
KW - Visual analytics
KW - Visual knowledge discovery
UR - https://www.scopus.com/pages/publications/34948837189
U2 - 10.1109/VAST.2006.261439
DO - 10.1109/VAST.2006.261439
M3 - Conference contribution
AN - SCOPUS:34948837189
SN - 1424405912
SN - 9781424405916
T3 - IEEE Symposium on Visual Analytics Science and Technology 2006, VAST 2006 - Proceedings
SP - 129
EP - 136
BT - IEEE Symposium on Visual Analytics Science and Technology 2006, VAST 2006 - Proceedings
T2 - IEEE Symposium on Visual Analytics Science and Technology 2006, VAST 2006
Y2 - 31 October 2006 through 2 November 2006
ER -