Information Visualization Main Concepts and Techniques

Silvia Batraneanu

MSPAS 2009

Reference model for visualization

Data Types

Nominal Equal or not equal than others, order does not matter

Ordinal Order is important, they can form ordered sets or

tuples

Numerical can do arithmetic operations on them spatial, geophysical, temporal

Data Transformations

Involves loss (filter, aggregation) or gain (revealing another aspect) of information

New values can be derived: Mathematical operations: sum Statistical operations: mean

The data type can change: Numerical -->Ordinal : classing Nominal --> Ordinal : sorting

Data Tables

Structured information ready to be mapped to visual structures.

We know : •the number of attributes dimensionality•the types of the attributes

Visual Structures Spatial substrate – perceptually dominant

Main aspect : axes Types of axes: nominal, ordinal, quantitative

Marks(glyphs) 4 elementary types:points, lines, areas, volumes Take up space

Marks’s graphical properties Spatial : size, orientation Object related: Grayscale, Color, Texture, Shape Some are more effective than others

Visual Mapping Example - FilmFinderInteractive controls

•Ratings --> Oy•FilmType->Nx

Scatterplot •Year Qx•Popularity Qy.•FilmIdP(Year, Popularity)•FilmID(FilmType) -> P(Color)

Encoding more information with spatial position

CompositionAlignmentFoldingRecursionOverloading

AlignmentRepetition of the axes at a different position in space

FoldingContinuation of the axis in an orthogonal dimension

Recursion

Repeated subdivision of space

Trees – using connectivy

Cone tree

Classical representation

Tree view

Trees – using enclosure

Treemap

Networks The most complex data structures that need to be

visualized

Used to describe telecom and computer networks, World Wide Web

Contains cycles sole encoding technique : connectivity

Graphs containing just tens of nodes tend to look like a ball of tangled strings map clutter

Important aspects in visualizing networks

Positioning the nodes key to readability Geographical position: SeeNet Graphical layout algorithms : mininization of link crossing Advanced iterative positioning algorithms : spring embedder, planar straight line embedding

Managing the links so they convey information Color the links according to type or utilization: SemNet Select a link subset with the help of interactive sliders: SeeNet Represent a pair of directed arcs as a single arc: SeeNet

Handling large-scale graphs -> uses some form of aggregation Hierarchical clustering : SemNet Geographical clustering: SeeNet Clustering based on natural hierarchy : software systems

Interacting with and navigating through large networks Levels of detail can enable nonuniform and interactively progressive aggregation

SeeNet

Efficient display

Mapclutter

Call volume aggregation

Grouping two links into one

Spring embedding

Before

After

Narcissus

Using 3D: Pros and ConsPros: more effective for 3D physical data additional dimension for encoding data can accommodate large-scale visual structures

Cons: greater implementation challenges: shading,

lighting, navigation, occlusion significantly more processing power

View transformations

Location probes Details on demand Brushing Magic lenses

Viewpoint controls Zoom, pan, clip Overview+detail

Distorsions (Focus+Context) Bifocal : Perspective Wall, Document Lens Polyfocal : Table Lens Based on levels of interest: Fish Eye Lens

Levels of Detail

Details on Demand

Popup windows Adjacent frames

Brushing

Magic lens

Overview + detail

Distorsion

Monofocal Bifocal Polyfocal Fisheye

Representationspace

Distortedspace

Transfer functionTransfer function

Magnification function

Bifocal view – Perspective wall

Bifocal view – Document Lens

Polyfocal view –Table lens

Fish-eye lens

Normal view Distorted view

Levels of Detail

Overview+detail vs. Focus+Context

Overview+detail Simple to implement and understand Operators need to shift attention between the two windows Typical zoom: 5-15 scaling to 1000 with intermediate views

Focus+context Harder to implement and understand Effective if critical features remain undistorted Keeps overview and details visible at all times Typical zoom: 2-5

Interaction

Changes the process of understanding dataAllows the user to explore more possibilities in a given timeHas to be sufficiently fast to be efficient

Time and Interaction

Three levels of interaction Finest level – psychological moment – 0.1 s

Stimuli fuse in a single percept Animation breaks down if longer than 10 frames/second.

Intermediate level – unprepared response – 1s The user has a minimum of time to respond

Coarsest level – unit task –about 10 sec Can do a minimal unit of cognitive work

Animation must be slowed down in some cases

Interactive visualization systems need scheduling mechanisms

Interacting with Data Transformations

Dynamic Queries Uses interactive controls to filter data

Direct Walk Navigates from record to record through linking ->Web

browser

Details On Demand

Attribute Walk Searches for objects with similar properties as a selected one

Brushing

Interacting with Visual Mappings

Data FlowUses explicit node-link diagram to represent

the mappings Pivot tables

Lets the user rapidly manipulate the mapping of data to rows and collumns

Interacting with View Transformations

Direct selection

Camera movement

Magic lens

Overview+detail

Zooming

Visualization levels of useInfosphere Information workspace

Visually enhanced objectsVisual knowledge tools

We learned about..

The different phases in the information visualization process

Improving the use of space and displaying more variables

Displaying complex structures such as trees and networks

The main visual transformations Interacting with a visualization system

Bibliography

“Readings in information visualization: Using vision to think”, S.K. Card, J.D. MacKinlay, B. Schneiderman, 1999, Morgan Kaufmann Publishers

“Information Visualization: Beyond the horizon”, 2nd ed, Chaomei Chen, Springer

“Information Visualization”, Robert Spence, 2000, Addison-Wesley

http://www.infovis-wiki.net/ IEEE Information Visualization Conference

http://vis.computer.org/

Advanced real-time visualization system for ATLAS TDAQ networks

ATLAS TDAQ system Selects interesting events coming

out of the ATLAS detector: 64 TBytes/s tens of Mytes/s

Uses a three layer trigger architecture

Uses three distinct switched Ethernet networks Data FrontEnd Data BackEnd Control

The three networks total over 4000 ports, 2500 PCs, 200 edge switches and 5 chassis core switches

ATLAS-Specific Visualization Requirements

A comprehensive monitoring system provides large amounts of data -> what data to present to who and how

Three types of consumer groups: Networking experts System analists Operators

The ideal visualization system would: Follow the system architecture and its data flow Display both traffic and status info in real-time Allow efficient navigation so as not to lose the big picture

Available 2D display solutions have proven to be very limited

3D visualization system

Chose 3D because it can cope better with large scale models

Profited from recent advances: levels-of-detail, fly-through navigation, proximity sensors etc

Used X3D standard: Can be extended through prototypes Has an external Scene Access Interface Chosen X3D browser : Octaga Player

Modeled a hierarchical 3D model for the TDAQ system

Hierarchical 3D model

Top layer Overall picture at a glance Contains processor farms and network cores

Middle layer Contains processors and edge switches grouped by

functionality Bottom layer

Contains individual ports and their associated traffic statistics and plots

Port level view

Ox time (last 5 intervals)Oy traffic (top) and errors (bottom)Color traffic state( Critical, Stressed, Normal) Uses both alignment and recursion

Details on Demand (additional port info, traffic plots)

Device level view

Control switch Data switch

Processor

Each device displays:•Traffic panels •Overall status light – its state/color is a result of statistics aggregation

Used different colors to distinguish data and control networks

Farm level view

Each rack has:

•Multiple processors •A data switch (top)•A control switch (bottom)

Due to natural aggregation at the rack level, we were able to use proximity instead of connectivity eliminated map clutter

WALK navigation is used inside a farm

Top level view Uses connectivity to display

the relationships between subsystems

Shape differentiates the subsystems: Boxes Farm processors Cylinders Network Cores

Color differentiates the networks: Yellow : Data Front End Red: Data BackEnd Green:Control

Uses FLY navigation

Important characteristics

Uses a prototype for each object type scene automation and expansion

Refreshes the scene at 30 seconds Uses levels of details to be able to render the

model Combines FLY and WALK navigation types with

proximity sensors to accomplish smooth fly-through navigation