to macroscopic changes in decision strategy [13] and, indeed, we
contend it is this regularity that provides the key opportunity for
visualization technologies. For example, it is known that requiring
users to mouse-over icons in order to reveal decision critical
information reduces the amount of information that users retrieve,
despite the fact that it only adds hundreds of milliseconds to the
interaction. More interestingly, mouse-over designs can shift users
from using non-compensatory to more compensatory strategies.
Conversely, presenting too much information all at once leads to
visual ‗crowding‘ and the potential for feature swap, e.g.
numerical transposition errors, and therefore error.
Visualization technologies work not simply because they are
visual, but because, by enhancing the efficiency with which
people can compare results, visualization can fundamentally
modify the processes by which decisions are made. In the
proposed system for proactive decision-making, visualization
design will emphasize comparison, as others have done, but will
do so as directed by recent theory in the cognitive sciences [21].
We also need to push beyond the individual. While much research
on visualization has focused on understanding the performance of
individuals engaged in diagnosis tasks, we contend that there is
considerable potential for new insights for the design of
collaborative visualization technologies. Visual Analytics is not
simply the visualization of the output from analysis processes, but
the creation of insight in the decision-makers working with these
visualizations, that is, the analysts are active participants in
constructing the manner in which these data are to be processed,
creating and revising associations between parts of the dataset by
manipulating the graphical user interface [7].
To develop visual analytics for decision support in Big Data
applications, we will apply concepts and principles from
Ecological Interface Design [18]. ‗Ecological Interfaces‘ are
designed to visualize the manner in which physical components of
the system map onto the (more abstract) functions that the system
performs. So, they are views of the process which are not simply
maps of how physical components connect to each other but are
abstractions which show how types of physical components affect
particular functions. The purpose of such designs is to improve
operator decision-making and diagnosis when dealing with faults
relating to those specific functions. For our system, this means
that the visualization will not only display the model‘s input and
output, but also the relationships between elements in the decision
space. One element of Ecological Interface Design is simply the
reflection of the constraints in the work domain through
constraints in the user interface. In this way, the ‗ecologies‘ of the
work domain, of the environment and of the organization become
reflected in the user interface through the definition and
management of these constraints. Added to these ecological
constraints are constraints from the analyst/modeler, such as
expectations and mental models.