Selective Dynamic Manipulation
of Visualizations
Chuah, Roth, Mattis, Kolojejchick
Motivation
• Need 3D techniques for interactive visualizations
of multidimensional data. We want:
– Selective: A high degree of user control
– Dynamic: Interactions all occur in real time,
with animation
– Manipulation: Users can directly move and
transform objects in the visualization
• Author’s system is called SDM
Barriers
• Many data sets have too much information
to be on screen at once
• Much clutter and occlusion (hidden data) in
dense sets of data
• Difficult to give a sense of scale: some
objects may be completely dwarfed by
others (green objects in Fig 1)
More Barriers...
• Must be able to classify data into sets and
save those classifications
• Must be able to compare quantities which
are not near each other (difficult to compare
heights, for example, if they are at different
distances from the user [Fig 3])
• Authors believe SDM deals with these
issues
Sample Data
• Crisis relief network
– Supply centers are cylinders
– routes between them are dark lines on the floor
– shelters where supplies are needed are
rectangular bars
– heights of cylinders and bars indicate supplies
available or needed
– Arranged in a network, like Becker paper
SDM components
• Object centered selection
– the selected set is made up of objects instead of
a spatial area
– can click on desired objects or use our old
friend the constraint slider
– when you create them, you can save and name
them
More SDM components
• Dynamic operations
– The user uses a “physical” handle to manipulate
the data (Fig. 4)
– Attach a handle to an object, and push or pull
on it: causes the object, or a set of objects, to
grow, shrink or move
– can control one or more parameters with single
handle
Constraints
• Context persistence
– SDM maintains a relation between the set being
manipulated and the original set.
• Set wide operations
– if you can move or scale one object in a focus
set, you can move or scale any.
Feedback Techniques
• SDM must clearly identify the selected set
– so we know what objects will change if we take
an action
• SDM must maintain scene context
– if we change something, a “shell” of the
original value is left in its original place.
More Feedback Techniques
• Maintain Temporal Continuity
– They use animation to allow the user to see
what has happened without having to think too
hard about it
• Maintain relationship between selected set
and environment
– Keep a scale of the differences on screen, for
example
• Allow objects easily to be returned to their
original positions
How Do We Use It?
• You can tell selected set apart by color or
width
• You can view occluded objects by
– elevating them (Fig 9) - lose context
– making all other objects invisible - lose context
– making all other objects of height 0 (Fig 10) lose context
– make all other objects very thin (Fig 11) - still
partially hidden
– make other objects transparent
Favorite Sentence
• … the “physics” provided by SDM is not
limited to real world manipulations; users
can also elevate, compress, and perform
other operations upon objects that wouldn’t
be possible with actual physical models.
How do we use it? (cont)
• For different data sets, can use different
scales
– This is so that data sets with much greater or
lesser values do not dominate
• Can interactively make and visualize new
classes of data
– This is a lot better than having to update the
entire database first
How Do We Use It?
• To solve the problem of comparing things at
different distances, sets of data can be
brought to the front and compared in two
dimensions (Fig 13)
Strengths
• Enables a more precise, quantitative
comparison between objects
– preserves relationships between focus objects
and rest of data
– scaling is kept correct
– distortions and occlusions of 3D are overcome
• Also, it is pretty cool
Weaknesses
• Can only view limited part of the data set:
the rest may be “in the distance” (possibly
add rotation)
• Can still get occlusion problem if focus set
is dense
• Does not address multimedia, UI, how to
decide on representation?
Continuing Efforts
• Sage research project
– SDM’s “physicalization” of the abstract space
is combined with automated visualization tools,
multimedia and UI stuff to create an entire
system
Externalizing Abstract
Mathematical Models
Tweedie, Spence, Dawkes, Su
The Problem
• Mathematical models are important in many
domains
• They are often quite complex, not having an
obvious physical visualization
– an example of an obvious one would be a flow
model might into a network or a pipe
• How can we visualize them?
The Solution
• Interactive Visualization Artifacts (IVAs)
– Instead of visualizing the raw data, we visualize
precalculated data as 2 kinds of data
• a description of the physical nature of an artifact,
called parameters
• a description of the results we can expect from an
artifact, called performance criteria
• We develop different IVAs to handle any
given problem - we describe 2
Our Example
• The Light Bulb
– design parameters: filament width, filament
material
– performance criteria: cost, brightness, lifetime
• But there are problems
The Problems with Light Bulbs
• We need to create a light bulb given the
performance data: but there is no way to get
the parameters given the performance data
(except trial and error - ugh!)
• Changes in manufacturing mean that any set
of parameters can only be guaranteed to be
in a range of values - but not exact values
More Problems with Light Bulbs
• Often, you also have to maximize some
other objective, like manufacturing yield.
IVA One: The Influence Explorer
• We precalculate the data and display histograms
based on it [Fig 6]
• Each bulb design is represented once for each
parameter and criterion: the design goes in the
appropriate bin
• The upper and lower limits on the sliders can
represent the desired limits (Red passes all
performance requirements, and black to white
indicates it has failed some)
The Influence Explorer
• If we also want to chart performance and
parameters, we can do so as in Fig 7
• Red is correct for all
• Blue means it fails some performance
requirement (thus will reduce yield, but can
still be made)
• Black, gray or white means it has failed one
or more performance and/or parameter
requirement
Influence Explorer
• This color coding shows how altering the
criteria will help
• Keep playing around until yield (which is
computed and shown) is high
IVA 2: The Prosection Matrix
• Provides a scatterplot for each possible pair
of parameters [Fig 13, 14]
– This is a 2D PROjection of a SECTION of ndimensional parameter space.
Prosection Matrix, cont
• Values are chosen at random to be projected
on the scatterplot from the performance
requirements given
– Can adjust the sliders to determine the
acceptable performance requirements
– Place a bounding box in the section to
determine ranges of parameters
Strengths
• Reasonably effectively maps multivariable
data into 2 dimensions
• Can transform a complicated problem into a
much simpler one
• Influence Explorer is partially analogous to
parallel coordinates
– can use intuitions from that representation
Weaknesses
• Some of these problems can reasonably be
automated (hill climbing algorithms, etc)
• Prosection matrix makes you reduce
problems to pairs of criteria
– counter-intuitive projection representation
• May not effectively handle large numbers of
variables
– (n2 - 3n + 2) /2 prosection matrices is a lot.