Multi-Dimensional Data Visualization

cs5764: Information Visualization

Chris North

Review

• What is the Visualization Pipeline?

• What are the steps of Visual Mapping?

• What is the Info Vis Mantra?

Information Types

• Multi-dimensional: databases,…

• 1D: timelines,…

• 2D: maps,…

• 3D: volumes,…

• Hierarchies/Trees: directories,…

• Networks/Graphs: web, communications,…

• Document collections: digital libraries,…

The Simple Stuff

• Univariate

• Bivariate

• Trivariate

Univariate

• Dot plot

• Bar chart (item vs. attribute)

• Tukey box plot

• Histogram

Bivariate

• Scatterplot

•

Trivariate

• 3D scatterplot, spin plot

• 2D plot + size (or color…)

Multi-Dimensional Data

• Each attribute defines a dimension

• Small # of dimensions easy• Data mapping, Cleveland’s rules

• What about many dimensional data? n-D

What does 10-D space look like?

Projection

• map n-D space onto 2-D screen

Glyphs: Chernoff Faces

• 10 Parameters:• Head Eccentricity

• Eye Eccentricity

• Pupil Size

• Eyebrow Slope

• Nose Size

• Mouth Vertical Offset

• Eye Spacing

• Eye Size

• Mouth Width

• Mouth Openness • http://hesketh.com/schampeo/projects/Faces/chernoff.html

Glyphs: Starsd1

d2

d3

d4d5

d6

d7

Multiple Views withBrushing-and-linking

Scatterplot Matrix

• All pairs of attributes

• Brushing and linking

• http://noppa5.pc.helsinki.fi/koe/3d3.html

… on steroids

Different Arrangements of Axes

• Axes are good – Lays out all points in a single space– “position” is 1st in Cleveland’s rules– Uniform treatment of dimensions

• Space > 3D ?

• Must trash orthogonality

Parallel Coordinates

• Inselberg, “Multidimensional detective” (parallel coordinates)

•

Parallel Coordinates

• Bag cartesian

• (0,1,-1,2)=

0

x

0

y

0

z

0

w

Star Plot1

8

7

6

5

4

3

2

Parallel Coordinates with axes arranged radially

Star Coordinates

• Kandogan, “Star Coordinates”•

Star Coordinates

Cartesian Star Coordinates

d1

d2

d5

d6

d8

v1

v2

v3

v4

v5

v6

v7

v8

p

d7 d3

d4

P=(v1,v2,v3,v4,v5,v6,v7,v8)P=(v1, v2)

v1

v2

d1

d2

p

Mapping:• Items → dots• Σ attribute vectors → (x,y)

Analysis

Table Lens

• Rao, “Table Lens”•

FOCUS / InfoZoom

• Spenke, “FOCUS”•

VisDB

• Keim, “VisDB”•

Pixel Bar Charts

• Keim

Comparison of Techniques

Comparison of Techniques

• ParCood: <1000 items, <20 attrs» Relate between adjacent attr pairs

• StarCoord: <1,000,000 items, <20 attrs» Interaction intensive

• TableLens: similar to par-coords» more items with aggregation

» Relate 1:m attrs (sorting), short learn time

• Visdb: 100,000 items with 10 attrs» Items*attrs = screenspace, long learn time, must query

• Spotfire: <1,000,000 items, <10 attrs (DQ many)» Filtering, short learn time

Multi-DimensionalFunctions

cs5764: Information Visualization

Chris North

Multi-Dimensional Functions

• y = f(x1, x2, x3, …, xn)

• Continuous:• E.g. y = x1

3 + 2x22 - 9x3

• Discrete:• xi are uniformly sampled in a bounded region

• E.g. xi = [0,1,2,…,100]

• E.g. measured density in a 3D material under range of pressures and room temperatures.

Relations vs. Functions

• Relations:• R(A, B, C, D, E, F)

• All dependent variables (1 ind.var.?)

• Sparse points in multi-d dep.var. space

• Functions:• R(A, B, C, D, E, F, Y) : Y=f(A, B, C, D, E, F)

• Many independent variables

• Defined at every point in multi-d ind.var. space (“onto”)

• Huge scale: 6D with 10 samples/D = 1,000,000 data points

Multi-D Relation Visualizations…

• Don’t work well for multi-D functions

• Example:• Parallel coords

• 5D func sampled on 1-9 for all ind.vars.

• Typically want to encode ind.vars. as spatial attrs

1-D: Easy

• b = f(a)

• a x

• b y

a

b

2-D: Easy

• c = f(a, b)

• Height field:

• a x

• b y

• c z b

a

c

2-D: Easy

• c = f(a, b)

• Heat map:

• a x

• b y

• c color

b

a

c

3-D: Hard• d = f(a, b, c)

• Color volume:

• a x

• b y

• c z

• d color

• What’s inside?

a

b

c

4D: Really Hard

• y = f(x1, x2, x3, x4, …, xn)

• What does a 5D space look like?

• Approaches:• Hierarchical axes (Mihalisin)

• Nested coordinate frames (Worlds within Worlds)

• Slicing (HyperSlice)

• Radial Focus+Context (PolarEyez, Sanjini)

Hierarchical Axes• 1D view of 3D function: (Mihalisin et al.)

f(x1, x2, x3)

x3

x2

x1

as in TableLens

5D9 samp/D

Hierarchical Axes• 2D view of 4D function (using heat maps)

• y = f(x1, x2, x3, x4)

• Discrete: xi = [0,1,2,3,4]

x1

x2

x3

x4

y = f(x1,x2,0,0) as color

Hierarchical Axes• Scale?

• 6d = 3 levels in the 2d approach

• 10 samples/d = 1,000,000 data points = 1 screen

• For more dimensions:• zoom in on “blocks”

• reorder dimensions

• 5D9 sample/D

Nested Coordinate Frames

• Feiner, “Worlds within Worlds”•

Slicing

• Van Wijk, “HyperSlice”•

Radial Focus+Context

• Jayaraman, “PolarEyez”

• infovis.cs.vt.edux1

x2x3

x4

x5

-x1

-x2-x4

-x5

Comparison

• Hierarchical axes (Mihalisin): •

• Nested coordinate frames (Worlds in Worlds)•

• Slicing (HyperSlice): •

• Radial Focus+Context (PolarEyez)•

Comparison

• Hierarchical axes (Mihalisin): • < 6d by 10 samples, ALL slices, view 2d at a time

• Nested coordinate frames (Worlds in Worlds)• < 5-8d, continuous, no overview, 3d hardware

• Slicing (HyperSlice): • < 10d by 100 samples, 2d slices

• Radial Focus+Context (PolarEyez)• < 10d by 1000 samples, overview, all D uniform, rays

Dynamic Queries

cs5764: Information Visualization

Chris North

HomeFinder

Spotfire

Limitations

• Scale:• Scatterplot screen space: 10,000 – 1,000,000

• Data structures & algorithms: < 50,000– Poor screen drawing on Filter-out

• A Solution: Query Previews!

• “AND” queries only• Arbitrary boolean queries?

• A solution: Filter Flow

DQ Algorithm

• Idea: incremental algorithm • only deal with data items that changed state

• When slider moves:• Calculate slider delta

• Search in data structure for data items in the delta region

• If slider moved inward (filter out):– Erase data items from visualization

• Else slider moved outward (filter in):– Draw data items on visualization Problem!

Overlapped items, erases items

underneath too

DQ Data Structures (1)

• Sorted array of the data for each slider

• Need counter for each data item = # sliders that filter it• Attribute Explorer visualizes these counters too!

• O(delta)

Year:

Delta

DQ Data Structures (2)

• Multi-dimensional data structure• E.g.: K-d tree, quad-tree, …

• Recursively split space, store in tree structure

• Enables fast range search, O()

DQ Data Structures (2)

• Multi-dimensional data structure• E.g.: K-d tree, quad-tree, …

• Recursively split space, store in tree structure

• Enables fast range search, O(logn)

Delta

Erasure Problem

• Each pixel has counter = number of items• Can visualize this for density!

• Z-buffer?

• Redraw local area only

Filter-Flow

BettyCatherineEdnaFredaGraceHildaJudyMarcusTom

Influence/Attribute Explorer

• Tweedie, Spence, “Externalizing Abstract Mathematical Models” (Influence/Attribute Explorer)

•

Query Previews

• Doan, “Query Previews”•