Institute Johann Bernoulli, Univ. of Groningen, the Netherlands
Title: Image-based information visualization (or how to unify SciVis and InfoVis)
Abstract:
For
decades, scientific visualization (SciVis) and information
visualization (InfoVis) have been related, but still distinctly
separated disciplines. Methods and techniques in the two areas have
developed relatively separately, causing an arguably unnecessarily
separation in the visualization field. Attempts for unification exist,
but are largely based on heuristics, and subject to critique from both
the SciVis and InfoVis angles. In this talk, we argue that this
separation is not necessary, and, up to large extents, artificial. More
specifically, we argue that the difference between SciVis and InfoVis
is not a matter of design decisions only, but, more centrally, a matter
of representing the structure of large data collections by means of
smooth, continuous, encodings. We present a way to cast InfoVis along
the same principles as the more classical SciVis, based on a
continuous, multiscale, spatial representation of data. Putting it
simply, we argue that visualizing large amounts of InfoVis data can use
encoding techniques which share the same continuity and multiscale
principles as most classical spatial SciVis (or image processing)
methods use. In turn, we show how this is possible by means of defining
appropriate similarity metrics and encoding principles for InfoVis
data. This leverages a wealth of data simplification, encoding, and
perception principles, since long available for SciVis data, for the
richer realm of InfoVis data. We demonstrate our image-based paradigm
by examples covering the visualization of relational, multidimensional,
and time-dependent InfoVis data.
