Selective Dynamic Manipulation of Visualizations Chuah Roth Mattis

Selective Dynamic Manipulation of Visualizations Chuah, Roth, Mattis, Kolojejchick

Motivation • Need 3 D 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 3 D 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 2 D 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 – (n 2 - 3 n + 2) /2 prosection matrices is a lot.