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Visual Analytics Jan 7 & 14, 2013 Vedran Sabol (KMI, TU Graz)

Visual Analytics

Multimedia Information Systems VO/KU (707.021/707.022)

Vedran Sabol

KMI, TU Graz

Jan 7 & 14, 2013


Visual Analytics

Structure of the lectures

• 7.1.2013

Motivation

Introduction to Visualization and Visual Analytics

Visualization examples and demos

• 14.1.2013

In depth analysis of selected Visual Analytics methods

Algorithms, visual interfaces, architecture

Problem solving with Visual Analytics - examples

2


Motivation

• We are confronted with:

Massive amounts of information

Dynamically changing data sets

Incomplete and conflicting information

Complex knowledge structures, relationships, networks

Multi-dimensional knowledge objects

Heterogeneous information

• Multimedia, geo-spatial, sensory data,…

• Structured and unstructured information

…

How can computers help us to understand and utilize our data?

Explore, analyse, understand

Unveil important facts and knowledge hidden within the data

3


Motivation Knowledge Discovery Process

• Knowledge Discovery Process [Fayyad, 1996]

Mainly an automatic approach consisting of a chain of processing steps

Goal: discovery of new, relevant, previously unknown patterns and relationships in data

4

Feedback Target Data

Transformed

Data

Patterns &

Models

Preprocessed

Data

Data

USER

Knowledge

Preprocessing & Cleaning

Data Transformation

Data Mining & Pattern Discovery

Interpretation & Evaluation

Data Selection


Motivation

• Machines are very powerful

Automatic processing methods for huge data sets

Exponential growth of computer-performance since 60 years

• Moor‘s Law: continues until 2020, 2030… ?

Distributed computing: Cloud, Grid, …

• Nevertheless, machines still behind humans in

Identification of complex patterns and relationships

Wide knowledge and experience

Abstract thinking

…

5


Motivation

• Human visual apparatus is an extremely efficient „processing machine“

• Enormous amounts of information are transferred by the visual nerve into the brain cortex

• Visual cortex remains unbeatable in recognition of objects and complex patterns (for example rotational invariance)

• Pre-attentive processing

6


Pre-attentive Processing

• Capability to process certain visual information without focusing our attention

• Criterion 1: Processing time < 200 - 250ms

Eye movements in about 200ms highly parallel processing

• Criterion 2: Processing time does not correlate with the amount of noise in the data

7



It is immediately possible to determine which data set contains a red spot Pre-attentive processing possible

8



It is still possible to quickly determine where the red spot is Borderline case

9



Scanning is necessary to determine where the red spot is Pre-attentive processing not possible

10


Motivation Visualization

• Solution

Employ the human visual system for pattern recognition

Use machines to transform the data into a suitable graphical representation

• Challenges

How should the graphical representations look like (design)?

Which operations shall be supported on the graphical representation (interactivity)?

How to compute the graphical representation (algorithms)?

11


What is Visualization?

Definitions

“Transformation of the symbolic into the geometric.” [McCormick et al., 1987]

“The depiction of information using spatial or graphical representations to facilitate comparison, pattern recognition, change detection, and other cognitive skills by making use of the visual system. “ [Hearst, 2003]

The use of visual representation to aid cognition

Graphical representation of data, information and knowledge

Use of human visual system, supported by computer graphics, to analyze and interpret large amounts of data

…

12


Visualization Examples

13


Visual Analytics

14

• Abundance of data:

• problems are becoming too large to be addressed by visualization alone

• Limited resources of the visual front end

• Combine machine processing with human capabilities in a suitable way and get the best of both worlds.

• Integrate humans in the analytical process

• Provide means for explorative analysis

• Visual Analytics: a young research field (2005)


Visual Analytics

15

• Combines automatic methods with interactive information/data/knowledge visualisation to get the best of both worlds [Keim 2008]

• Supports analytical reasoning facilitated by interactive visual interfaces [Thomas 2005]

• Focuses on interaction between humans and machines through visual interfaces to derive new knowledge

Repository

New Insights and Knowledge

Algorithms Visualization


Visual Analytics

16

• Main Idea (Mantra): “analyse first – show the important – zoom, filter and analyse further – details on demand” [Keim 2008]

Initial analysis and visual pattern recognition

Posing a hypothesis

Further analysis steps (automatic and interactive)

Confirmation or rejection of the hypothesis: new facts

Confirm the expected, discover the unexpected

• Challenges [Keim 2009]

Balance between automatic and interactive analysis

Design of effective VA workflows

Data quality

Scalability


Visual Analytics in the Web

17

• HTML5 provides the basis for visualization in the Web

• Rich, responsive user interfaces

• AJAX

• New elements, advanced forms

• Rendering and visualization

• Canvas

• SVG

• Logic and Interactivity

• JavaScript

• Server-Client Web architectures fit the needs of Visual Analytics

• Model View Controller (MVC) architecture

• Data storage and crunching on the server


Visualization Design Representation Forms

• Fundamental categories of visual representation:

Formalisms

Metaphors

Models

• Formalisms: abstract schematic representations

Defined by a designer

Users must learn how to read and interpret

Example: Percentage is represented by an arc

18


Visualization Design Representation Forms

• Metaphors: representations based on a real-world equivalent

Intuitive

User can understand the meaning through building analogies

Example: using the geographic map metaphor to represent similarity in non-spatial data

• Models: based on mental representations of real physical world

Data typically has a natural representation in the real world

Examples: visualization of sensory data in 3D, virtual 3D worlds

19


Visualization Data – Information - Knowledge

• Data/Scientific Visualization

• Information Visualization

• Knowledge Visualization

20

Data Knowledge Information

Representation complexity, applicability by humans

Machine processing capability



• Data

Formal representation of raw, basic facts

Have a fixed format: numbers, dates, strings,…

Have a fixed, predefined meaning (i.e. no interpretation required)

„3162“ – Hotel room number (not a telephone number)

• Information

Result of processing, manipulation and interpretation of data

May not have a fixed format (unstructured or semi-structured)

Meaning is determined by interpretation within some context

“A small, white mouse” – a computer or a field mouse? (determined by context)

21



• Knowledge

Identified, organized and as valid recognized information

Representations of reality through abstract, domain-dependent models

Represented by formalized conceptual systems: Taxonomies, Thesauri…

Ontologies are formally defined knowledge representations consisting of concepts, relations and rules (axioms)

• complex graph-structures

22

Animal

Mouse is a Legs

has

Jerry is a


Visualization Systems Categorization Depending on Data

• Data/Scientific Visualization

• Sensory data

• 3D spaces

• Knowledge Visualization

• Knowledge models

• Information Visualization

• Document content: text and multi-media

• Multidimensional data sets

• Structures: hierarchies and networks (graphs)

• Temporal information

• Geo-spatial information

• Multiple data types/aspects

23


Data/Scientific Visualization

• Visualization of simulation or sensory data

have a natural representation in the real, physical world

• Applications in physics, medicine, astronomy, industry, …

24

Pressure coefficients [NASA] Coil magnetic field


Data/Scientific Visualization

25

Weather monitoring - wind direction Monitoring Myocardial Infarctions

using ECG data [University of Dublin]


Knowledge Visualization

• Knowledge Visualization is about using visual representations to present and transfer existing (explicit) knowledge between people [Eppler]

• The focus is on structured knowledge spaces

Concepts, relations, facts, attributes

Navigation along structures present in the knowledge model

• Use of metaphors is common

26


Knowledge Visualization Examples

27

Stairs of Visualisation [Eppler] (Let‘s Focus: http://en.lets-focus.com/ )

Research Map [Bresciani]

http://en.lets-focus.com/





Knowledge Visualization Examples

Gyro, Know-Center [Kienreich]

28

Cultural Heritage Visualization Ancient Theatres [Blaise]


Information Visualization

• Interactive visualization of abstract information spaces

Abstract information has no „natural“, real-world representation

Rely on metaphors and formalisms

• Goal: identifying patterns and relationships

Explorative analysis and navigation

Unveiling of implicit knowledge

• InfoVis Mantra [B. Shneiderman]

„overview first - zoom and filter - details on demand”

Compare to the Visual Analytics mantra

29


Visualization Examples Document Content Summary

MovieDNA [Ponceleon] TileBars [Hearst]

TagClouds, Know-Center [Seifert]

30


Visualization Examples Multidimensional Data

Scatterplot [Nowell]

(Demos: http://www.highcharts.com/demo/scatter, http://mbostock.github.com/d3/talk/20111116/iris-splom.html)

Parallel Coordinates [Inselberg]

(http://mbostock.github.com/protovis/ex/cars.html)

31

http://www.highcharts.com/demo/scatter

http://www.highcharts.com/demo/scatter

http://mbostock.github.com/d3/talk/20111116/iris-splom.html



http://mbostock.github.com/protovis/ex/cars.html

http://mbostock.github.com/protovis/ex/cars.html


Visualization Examples Multidimensional Data Similarity - Text

Know-Center [Sabol et al.]

Galaxies (SPIRE), PNNL [Wise]

32


Visualization Examples Multidimensional Data Similarity - Images

Image Similarity Layouts [Rodden]

33


Visualization Examples Hierarchies

TreeMaps [Shneiderman]

(http://philogb.github.com/jit/static/v20/Jit/Examples/Treemap

/example1.html#)

Hyperbolic Tree (InXight) [Lamping]

(http://ucjeps.berkeley.edu/map2.html)

34

http://philogb.github.com/jit/static/v20/Jit/Examples/Treemap/example1.html




http://ucjeps.berkeley.edu/map2.html

http://ucjeps.berkeley.edu/map2.html



35

InfoSky, Know-Center [Andrews et al.]

Circle Packing, D3 library (http://mbostock.github.com/d3/talk/20111116/pack

-hierarchy.html)

http://mbostock.github.com/d3/talk/20111116/pack-hierarchy.html






Walrus, CAIDA

Information Pyramids [Andrews]

36


Visualization Examples Graphs

37

Gephy, https://gephi.org/

Narcissus [Hendley]

(Web-Demos: • Small: http://mbostock.github.com/d3/talk/20111116/force-collapsible.html • Medium: http://sigmajs.org/examples/gexf_example.html)

http://mbostock.github.com/d3/talk/20111116/force-collapsible.html




http://sigmajs.org/examples/gexf_example.html




Visualization Examples Graphs

38

Edge-Bundling [Holten & van Wijk]

Concept Networks [Kienreich] (Know-Center)


Visualization Examples Temporal Data

39

Spiral geometry [Carlis] Perspective Wall [Mackinlay]


Visualization Examples Temporal Data

40

LifeLines [Plaisant] Themeriver, PNNL [Havre]

(Demos: http://vis4.net/labs/as3streamgraph/ , http://bl.ocks.org/4060954)

http://vis4.net/labs/as3streamgraph/

http://vis4.net/labs/as3streamgraph/

http://bl.ocks.org/4060954

http://bl.ocks.org/4060954


Visualization Examples Geo-Spatial Data

41

Google Maps

APA-Labs component, by Know-Center


Visualization Examples Geo-Spatial Data

42

LucentVision [Pingali 2001]

Planetarium, Know-Center [Kienreich]


Visualization Examples Multiple Data Aspects – Geo-Temporal

GeoTime, Oculus [Kapler]

43


Visualization Examples Multiple Data Aspects – Immersive 3D Environments

Starlight, PNNL [Risch et al.]

44

Caleydo, ICG, TU Graz [Lex et al.]


Visualization Examples Multiple Data Aspects – Coordinated Multiple Views

Coordinated Multiple Views

• Multiple visualizations “fused” into a single, coherent user interface

• Each visualization designed to convey a different aspect of the data

simultaneous navigation and analysis over multiple data aspects becomes possible

• Coordination of state an behavior

• interactions in one visual component influence all others

• Selection, filtering, visual properties, navigation, …

45


Coordinated Multiple Views

Spotfire DecisionSite [Schneiderman]

46


Coordinated Multiple Views Media Watch on Climate Change

ECOResearch Portal - Media Watch on Climate Change: http://www.ecoresearch.net/climate/

47

http://www.ecoresearch.net/climate/

http://www.ecoresearch.net/climate/


Web Visualization Toolkits and Rendering Libraries

• D3 (Data-Driven Documents): http://d3js.org/

Protovis: http://mbostock.github.com/protovis/

• JavaScript InfoVis Toolkit: http://philogb.github.com/jit/index.html

• Raphaël: http://raphaeljs.com/

• Charting: jqPlot: http://www.jqplot.com ; gRaphaël: http://g.raphaeljs.com/ ; NVD3:

http://nvd3.org/ ; canvaseXpress: http://canvasxpress.org/ ; High-Charts: http://www.highcharts.com/ …

48

http://d3js.org/

http://d3js.org/

http://mbostock.github.com/protovis/

http://mbostock.github.com/protovis/

http://philogb.github.com/jit/index.html

http://philogb.github.com/jit/index.html

http://raphaeljs.com/

http://www.jqplot.com/



http://g.raphaeljs.com/

http://nvd3.org/

http://nvd3.org/

http://nvd3.org/

http://canvasxpress.org/



http://www.highcharts.com/




Performance and scalability

• Scalability limited by hardware

Number of pixels on screen

Computing power of the client

• SVG: thousands of items

• Canvas: at least one order of magnitude better

• WebGL: potentially millions of items

– not officially part of HTML5

• Usability issues

Clarity of the representation may be compromised by clutter

Orientation and navigation in large data

• How to scale to large (huge) data sets

Millions (or billions) of data elements.

49


Level of Detail

• Variable level of detail (LOD)

Technique known from 3D environments

Decrease complexity of representation for “far-away” objects

• Coarse-grained view of the whole data space

• Provide more details when zooming-in

50


Geovisualisation Google Maps

51


Aggregation

• Abstract data sets do not have a geometric structure appropriate for LOD

• Structure and organize the data space hierarchically

• Compute a hierarchical visual representation

Coarse-grained view of the whole data space

Render more details as zooming in

Navigate and explore along the hierarchical structure

• Two examples in the following:

Text Visualization (unstructured data)

Semantic Graph Visualization (structured data)

52


Visualization of Text Data

• Text remains an essential data type in many domains

• Challenges

Text is not pre-attentive

Text is „non-visual“, i.e. has no „natural“ visual representation

Composed of abstract concepts and complex relationships between them

Described by a very high amount of features (dimensions)

Vague and ambiguous

• Synonyms, Homonyms

Context

• Interpretation by humans

53


Selected Visualization Techniques Visualization of Text Data

Questions

What are the dominant topics (and entities, such as organizations and persons) mentioned in the data set?

How do documents correspond topically to each other?

What are the relationships between the dominant topical clusters?

How do topical clusters relate in size?

How do topics develop over time (major trends)?

Are there correlations between topics, entities and temporal developments?

…

54


Projection Processing Pipeline Summary

Natural Language Processing, Feature Engineering

Text Data

Mathematical Model: Vector Space Model

Similarity/Distance Metrics

Similarities/Distances

Clu

ster

ing

&

lab

ellin

g

Projection Rendering

Aggregation (Hierarchy) “virtual table of contents” Geometry Visualization

55

../TextMining/kmeansdemo.jnlp


Feature Engineering

Identify features describing document content

Apply natural language processing (NLP) methods

• Sentence detection and part-of-speech (POS) tagging: nouns, verbs, adjectives…

• Named entity recognition (NER): organizations, persons, locations, dates…

• Stemming: reduce words to root form

• Stopword filtering

• …

“Organized by government, services of commemoration are being held in Germany to mark the end of World War I in 1918. ...”

56


Vector Space Model Bag of Words

Each document represented as vector of terms

• d1: “Services of commemoration are being held around the world to mark the end of World War I in 1918. ...”

• d2: “World War I (abbreviated as WW-I, WWI, or WW1), also known as the First World War ...”

• d3: “We offer world wide service”

57

servic commemor world end war

d1 1 1 2 1 1

d2 2 2

d3 1 1

Weighting (TF/IDF)

Feature selection Feature Vectors

Texts


Similarity Metrics

Ideally: semantic similarity (elevator = lift)

In practice: statistical similarity

• (Euclidean) Distance between vectors

– Between 0 and infinity

• Cosine similarity: depends on the angle between vectors

– Between 0 and 1

58

Distances Feature Vectors


Clustering Definition

Grouping of data points so that those in the same cluster are more similar to each other than to those in other clusters

Given a set of data points

Find groups C1 to Ck (k < n) of data points which optimize a given criterion

• Within Cluster Criterion: Maximize similarity (or minimize distance ) of data elements within one cluster

• Between Cluster Criterion: Minimize similarity (or maximize distance) of data elements from different clusters

59

},,,,{ 121 nn xxxxX


Clustering Cluster Representation

60

Centroid: sum of the data point vectors (center of gravity)

Medoid: „best“ data point in the cluster

• the one closest to the center of gravity

Selected subset of cluster elements

All cluster elements

Convex Hull

…


Clustering Labeling a Cluster

61

Labels enable the user to interpret the cluster

Computation of the most important features of a cluster

• Centroid-Heuristic: 5-10 features with the highest weight

• „similarity; clustering; k-means; centroid;“

Discriminative analysis between clusters

• Documents on computers “computer” will appear in each label

– Descriptive but useless for discriminating between clusters

• Instead use best features discriminating between data points

– Features frequently appearing only in a fraction of data points

– „operating systems“, „programming languages“ etc.


Clustering Application

62

Browsing document collections

• Apply clustering recursively to compute a cluster hierarchy

• Use the labeled hierarchy as “virtual table of contents”

Feature Vectors

Distances

Cluster hierarchy


K-means Clustering

Partitional method: partitions the data set into k clusters

Given: document term vectors, number of clusters k

Overview of the algorithm

1. Seeding: choose k documents, use their vectors as cluster centroids

2. Compute similarity of documents to centroids, assign each document to the most similar cluster

3. Centroid update: add documents vectors to the centroid with the highest similarity

4. Goto 2 until (i) no data points move between the clusters, or (ii) iteration count has reached a predefined threshold I

Converges to a local optimum

• Several passes over data usually sufficient (I very small)

63


K-means Clustering Properties

Disadvantages

• Number of clusters must be given a-priori

– Constrained by application scenarios (e.g. browsing)

• Sensitive to initial seed choice (often random)

Advantages

• Runtime complexity: O(Ikn)

– I & k << n, both usually with an upper bound: O(n)

• Creates hyperspherical clusters

– Good for high-dimensional spaces (e.g. text)

– May underperform in low-dimensional spaces

64


Ordination

How to visualize high-dimensional data sets

Projection into a „smaller“ (2D) visualization space which can be understood by users

• Navigation and explorative analysis in the projection space

Dimensionality reduction techniques

• Projection of the high-dimensional space into a lower dimensional

• Preservation of distances/similarities

– Related data items placed close together

• Usability and aesthetics play a role

65


Ordination Methods

Distance-/similarity-preserving methods

• Multidimensional scaling

• Input is a distance-/similarity matrix

• Dimensions of the low-dimensional space have no meaning and no relation to the original dimensions

Transformations of the feature space

• Principal Component Analysis

• Self Organizing Maps

• Input are high-dimensional feature vectors

• Dimensions of the low-dimensional space may be related to the high-dimensional space

66


Multidimensional Scaling Motivation

Example: Dissimilarity between car makers

Which car makers are similar?

Which car makers build groups?

Impossible to read from large distance(/similarity) matrices

Siehe http://www.wiwi.uni-wuppertal.de/kappelhoff/papers/mds.pdf

67


Multidimensional Scaling

Projection into a 2D space with preserving the original distances

See: http://www.wiwi.uni-wuppertal.de/kappelhoff/papers/mds.pdf

68

http://www.wiwi.uni-wuppertal.de/kappelhoff/papers/mds.pdf






Multidimensional Scaling

69

See: http://www.wiwi.uni-wuppertal.de/kappelhoff/papers/mds.pdf







Multidimensional Scaling Example: 2D to 1D space

1 2

3

a

b

c

1 2 3

~a

~b

~c

x y

1 0.2 1

2 0.5 1

3 1.5 0.2

a= dist(1,2)= 0.3 b= dist(2,3) = 0.6 c= dist(1,3) = 1.55

Distance computation

1 2 3

1 0 0.3 1.55

2 0.3 0 0.6

3 1.55 0.6 0

Information loss is inherent!

70


Multidimensional Scaling Force-Directed Placement

• Heuristic, iterative multidimensional scaling method

• Spring model simulates a physical system

Computes positions and forces between objects

Force depends on similarity between objects in the original space

Similar object attract, dissimilar objects repulse each other

• Advantages: simple to implement, parametrizable, good layout quality, suitable for visualization

• Issues:

Tends to get stuck in local minima

Not scalable: O(n3) time-complexity for the brute-force version

71


Force-Directed Placement Algorithm

• Iteration: for each document i

For every other document j

totalForce_i += force(i, j)

Move object into the direction of the total force

Stop condition:

object movements have subsided, positions have stabilized sufficiently

Alternative: stress computation (computationally intensive)

• Layout quality evaluation: stress measure

Difference between pairwise distances in high- and low-dimensional spaces

72


Force-Directed Placement Basic Force Model

),(),(),( jihighjilowji dddistdddistddforce

2,,

2,, )()(),( yjyixjxijilow dddddddist

ji

jilowjihighji dddistdddistN

ddstress 2)),(),((1

1),(

km

kjkijihigh wwdddist2

,,),(

• Attempts to reconstruct the original distances low-dim space

• Scaling of distances may be unsuitable for visualization

• No parameterization possibilities

lowhigh distdist

idjd

lowhigh distdist

id jd

73


Force-Directed Placement Improved Force Model

gravdddist

pddsimddforce

r

ji

d

jiji ),(

),(),(

Similarity in original space

Distance in projection space

Constant

22 ||||*||||),(

ji

ji

jivv

vvddsim

Repulsive force

• First term: attractive force proportional to similarity

• Second term: rapidly rising, short-distance range repulsive force

Prevents „gravitational collapse“ of similar items

• Third term: weak cohesive force to prevent endless expansion of non-similar data elements

• Parameterization possibilities usable, visually appealing layouts

74


Force Directed Placement Item Position Computation

N

ijj

ijjiii xdxdddforceN

xdxd,1

)..)(,(1

1..

N

ijj

ijjiii ydydddforceN

ydyd,1

)..)(,(1

1.. id

1d

3d

2d

Force

Resulting Force xdxdxdxdxdforce

xdxdxdxdxdforce

iijii

jijii

.)..(0..0

.)..(1..1

• More complex models consider additional physical components

Friction, viscosity, acceleration, momenta, …

Requires solving differential equations

Computationally very intensive, but hardly layout improvements

75


Force Directed Placement Addressing Scalability Issues

• Solution: do not compare every object with all the others

Stochastic Sampling (neighbor + random sets) [Chalmers 1996]: O(n2)

Use kernel (sampling), pivots and interpolation [Alastair & Chalmers 2004]: O(n1.2)

Apply sampling and interpolation recursively [Jourdan & Melançon 04]: O(n*log(n))

Compute a hierarchy automatically using clustering, apply FDP along the hierarchy [Muhr, Sabol, Granitzer 2010]: O(n*log(n))

Hierarchical geometry: support for LOD and navigation

• Alternative Techniques:

Least Square Projection, Random Projections, FASTMAP, IDMAP…

Fast, but mostly inferior layout quality, often not visually pleasing layouts

76


Scalable Projection Algorithm

Input: term vectors, base area (rectangle)

Output: hierarchy of nested areas, 2D document positions

Recursive Algorithm:

• Aggregation: k-means clustering, labeling using highest weight features

• Similarity layout: force-directed placement, inscribing into area

• Area subdivision: Voronoi diagrams

• For each cluster: cluster size > threshold?

– Yes: apply algorithm recursively on the cluster

– No: layout documents (similarity layout)

77


Scalable Projection Algorithm Hierarchical Projection

78


Scalable Projection Algorithm Advantages

• FDP applied on small number of objects: good layout, fast

• Hierarchical labeled geometry

Navigation and exploration along the hierarchy

Labels adapted to the level of detail, from overview to detail

• Incremental: data set changes integrated seamlessly into the layout

User can recognized unchanged parts of the visualization

• Scalable

Time and space complexity: O(n*log(n))

Parallelization fairly straightforward

• Parameterizable

Adaptable to different data types

Produces visually pleasing layouts

79


Information Landscape Visualization

80

• Proximity expresses relatedness

• Hills represent groups of similar data elements

Height indicates size

Compactness indicates topical cohesion

• Labels capture essence of undelaying data

Orientation and navigation

400.000 documents


Hierarchical Information Landscape Navigation and Orientation

81

• Conveys relatedness and hierarchy

• Level of detail-sensitive navigation and orientation

Animated transitions: auto-focus on the chosen cluster


Topical-Temporal-Metadata Analysis Visual Interface

Know-Center [Sabol et al.]

82


Topical-Temporal-Metadata Analysis Coordinated Multiple Views

83

View coordination

• Colors and transparency

• Icons

• Size

• Selection

• Visibility

• Navigation in the hierarchy

Views

• Cluster hierarchy: tree

• Topical similarity: hierarchical information landscape

• Position in the hierarchy: location bar

• Topical trends: stream-view

• Facetted metadata hierarchy: tree

• Document metadata: table

• Document content: text pane


Topical-Temporal Analysis Example (Demo)

84

“Japan, Tokyo, Bay” cluster (red)

• 2 temporal peaks

• Topically separated (different hills)

Hypothesis: two different events

Analysis for validation:

• Inspection

• Searching + highlighting

• Correlating metadata


Visual Scatter/Gather Drill Down

• Identify and select relevant parts of the data set

• Retrigger analysis to focus on the chosen subset

85


Dynamically Changing Repositories

• Data set change over time

Data elements added, removed, modified

• Consequence: visual representation must change

• Problem: ensure visual representation change is appropriate

Magnitude of visualization change proportional to magnitude of data change

Only areas corresponding to modified data should change

Other areas of the visualization remain (mostly) stable

User retains recognition within a changing visualization

86


Dynamically Changing Repositories Incremental Integration of Changes

• Change in the layout corresponds to change in the data

User retains recognition and orientation through unchanged parts of the topography

87


Scalable Graph Visualization Motivation

• Large graphs increasingly common

Social networks

Semantic knowledge bases

Interlinked document repositories

• Need visual approaches for gaining insight into large graphs

• Challenges

Clutter caused by many nodes and intersecting edges

Limited load on the client

• Web and mobile clients

88


Scalable Graph Visualization Goals

• Provide an overview of the whole graph

Show the overall structure

• Avoid user and client overload

Introduce more details when zooming (LOD)

• Maximize clarity: apply techniques for avoiding overlap and clutter

Edge bundling, edge routing

• Combined Approach

Hierarchical aggregation of graph data (nodes and edges)

Clutter reduction: edge routing and bundling along the hierarchy

89


Scalable Graph Visualization Hierarchy Generation

• Aggregate nodes to meta-nodes (top-down)

1. Hierarchical clustering

• Node similarity depends on connectivity

2. Hierarchy Extraction from Ontologies

• Class hierarchy: traverse nodes/relations of particular types

• Aggregate edges to meta-edges (bottom-up)

Combine edges propagating outside of a cluster (inter-cluster edges)

90


Scalable Graph Visualization Clutter Reduction Techniques

• Force-directed edge bundling [Holten & van Wijk 2009]

Bundle edges propagating in “similar” direction

• Edge routing along the Voronoi mesh [Lambert et al. 2010]

Dijkstra's shortest path algorithm

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Scalable Graph Visualization Edge Bundling – non-Hierarchical vs. Hierarchical

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• Reduces edge clutter

• Some node-edge overlap remains


Scalable Graph Visualization Edge Routing – non-Hierarchical vs. Hierarchical

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• Reduces edge clutter and eliminates edge-node overlap

• Price: massive edge overlap on Voronoi boundaries

Edge stroke indicator for number of overlapping edges


Scalable Graph Visualization Level of Detail (Demo)

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• Client loads more detailed geometry on demand

Unternehmensgedächtnis &...

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