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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
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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 n-dimensional 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.