information motivation - data explosion - unifr.ch · 2011. 9. 28. · infovis seminar 2006...

InfoVis seminar 2006

InformationVisualization

Information Information VisualizationVisualizationSeminaireSeminaire DIVA 2006DIVA 2006

IntroductionIntroduction

Denis Lalanne

Re-visiting materials from Enrico Bertini, John Stasko and from my own.

Denis Lalanne / UniFr 2InfoVis seminar 2006

InformationVisualization Motivation Motivation -- Data ExplosionData Explosion

Society is getting more complexThere simply is more “stuff”

Computers, internet and web give people access to an incredibleamount of data

news, sports, financial, purchases, etc...

How much data?Between 1 and 2 exabytes of unique info produced per year

1000000000000000000 (1018) bytes250 meg for every man, woman and childPrinted documents only .003% of total

Peter Lyman and Hal Varian, 2000Cal-Berkeley, Info Mgmt & Systemswww.sims.berkeley.edu/how-much-info


InformationVisualization Motivation Motivation -- Data Data OverloadOverload

How to make use of the data?How do we make sense of the data?How do we employ this data in decision making processes?How do we avoid being overwhelmed?

The Need is ThereIn five years, 100 million people will be using an information-visualization tool on a near-daily basis. And products that have visualization as one of their top three features will earn $1 billion per year. – Ramana Rao, founder and chief technology officer, InxightSoftware Inc., Sunnyvale, California.

The problem:

DataDataData Data transfertData transfert

Web,Books,Papers,emails,

Scientific data,Biotech,ShoppingPeople

Stock/financeNews Vision: 100 MB/s

Ears: <100 b/sHaptic/tactile

SmellTaste

How??


InformationVisualization HumanHuman VisionVision

Highest bandwidth senseFast, parallelPattern recognitionExtends memory and cognitive capacityPeople think visually

The ChallengeTransform the data into information (understanding, insight) thus making ituseful to people

Example:Which state has the highest income?Is there a relationship between incomeand education?



Per capita income

Co

lleg

e d

eg

ree %



VisualizationVisualization isis anan old old practicepractice ……


InformationVisualization TheThe terribleterrible fate of fate of NapoleonNapoleon’’s s armyarmy

The classic C.J.Minard’s Napoleon’s army in Russia (drawn in 1861) [Tufte’s Visual Display of Quantitative Information]

Size of the army longitude temperatureDirection latitude date



SemanticSemantic fisheyefisheyeViewView ofof thethe worldworld by a New by a New YorkerYorker


InformationVisualization VisualizationVisualization isis aroundaround usus

... in Everyday tools



InfoVisInfoVisisis

((ScreenScreen--BasedBased))

VisualizationVisualization + Interaction+ Interaction


InformationVisualization Information Information visualizationvisualization

What is “information”?Items, entities, things which do not have a direct physicalcorrespondenceExamples: baseball statistics, stock trends, connections betweencriminals, car attributes...

What is “visualization”?The use of computer-supported, interactive visualrepresentations of data to amplify cognition. From [Card, Mackinlay Shneiderman ‘98]

What It’s Not InfoViz?Scientific Visualization

Primarily relates to and represents something physical or geometricExamples

– Air flow over a wing– Weather over Pennsylvania


InformationVisualization InfoVisInfoVis approachapproach

Provide tools that present data in a way to help people understand and gain insight from itClichés

“Seeing is believing”“A picture is worth a thousand words”

VisualizationOften thought of as process of making a graphic or an imageReally is a cognitive process

Form a mental image of somethingInternalize an understanding

“The purpose of visualization is insight, not pictures”Insight: discovery, decision making, explanation

Main IdeaVisuals help us

thinkExternalize cognition



The The classicclassic definitiondefinition

Information visualization is the use of computer-supported, interactive, visual representations of abstract data to amplify cognition.

[Card, Mackinlay, Shneiderman]


InformationVisualization RoughRough operationaloperational definitiondefinition

get some data and give them a visual representation(data items -> visual items, data features -> visual features)

provide some interaction facilities to explore and focus on interestingsubsets

+



FromFrom rawraw data data ……Car Dataset

7 dimensions (391 cars)miles per gallon (M.P.G.)number of cylindershorsepowerweightacceleration (time from 0 to 60 mph)yearorigin (USA, Europe, Japan)


InformationVisualization …… toto visualizationvisualization

7 dimensions (391 cars):miles per gallon (M.P.G.)number of cylindershorsepowerweightacceleration (time from 0 to 60 mph)yearorigin (USA, Europe, Japan)

Gain meaning, e.g. weight inversely proportional to acceleration

MPG

Cylinders

Horsepower

Weight

Acceleration

Year

Origin

Cars dataset: parallel coordinates depicting trends over 7 dimensions.


InformationVisualization …… and interactionand interaction

e.g., link & brush in multiple views


InformationVisualization WhyWhy isis InfovisInfovis usefuluseful??

We need to make sense of phenomena described by data, butraw data is hard/impossible to handle by humans

Infovis is perfect for exploration, perfect when the goal is vague

Other approaches to data analysis make exploration difficultStatistics:

(+) strong verification(-) does not support exploration and vague goals

Data mining: (+) actionable and reliable(-) black box style, not interactive (question-response)


InformationVisualization RolesRoles of of InfoVisInfoVis

ExplorationPrerequisite: domain knowledgeOutcome: new hypothesis

ConfirmationPrerequisite: hypothesisOutcome: confirmation/rejection (new hypothesis)

CommunicationPrerequisite: confirmed hypothesisOutcome: clear visualization

[Keim KDD’02]

Visual Analytics’ Motto: detect the expected and discover the unexpected


InformationVisualization TasksTasks in in InfoVisInfoVis

SearchFinding a specific piece of information

How many games did the Braves win in 1995?What novels did Ian Fleming author?

BrowsingLook over or inspect something in a more casual manner, seekinteresting information

Learn about crystallography

AnalysisComparison-DifferenceExtremesPatterns

Monitoring



InfoVisInfoVis GalleryGallery ......


InformationVisualization TreemapsTreemaps


InformationVisualization SunburstSunburst


InformationVisualization ParallelParallel CoordinatesCoordinates


InformationVisualization GraphsGraphs



Interaction Interaction ……


InformationVisualization DynamicDynamic FilteringFiltering


InformationVisualization LinkLink & & BrushBrush

EDV (exploratory data visualizer)


InformationVisualization DistortionDistortion ((fisheyefisheye viewsviews))



InfovisInfovis isis groundedgrounded on strong on strong perceptualperceptual principlesprinciples//studiesstudies ……


InformationVisualization Attention Attention andand perceptionperception


InformationVisualization DataData--featurefeature mappingmapping

get some data and give them a visual representation(data items -> visual items, data features -> visual features)


InformationVisualization DataData--featurefeature mappingmapping



InfoVisInfoVis seminarseminar themesthemes((listenlisten andand…… chose)chose)

1. Visual Perception & Graphical Principles2. High-Dimensional Data Representations3. Focus + Context4. Trees and Hierarchies5. InfoVis Systems and Toolkits6. Interaction techniques7. Taxonomies of Information Visualization8. Visualization with Data Clustering9. Temporal visualizations



1. 1. VisualVisual Perception & Perception & GraphicalGraphical PrinciplesPrinciples

Visual perceptionPre-attentive processing

SemioticsThe study of symbols and how they convey meaning

Related DisciplinesPsychophysics

Applying methods of physics to measuring human perceptual systems– How fast must light flicker until we perceive it as constant?– What change in brightness can we perceive?

Cognitive psychologyUnderstanding how people think, here, how it relates to perception

Perceptual ProcessingSeek to better understand visual perception and visual information processingMultiple theories or models existNeed to understand physiology and cognitive psychology



2. 2. HighHigh--DimensionalDimensionalData Data RepresentationsRepresentations

What happens to the following visualizations when you have lots and lots of data cases?

=> Becomes unreadable(e.g. parallel coordinates)



2. 2. HighHigh--DimensionalDimensionalData Data RepresentationsRepresentations

Many techniques for projecting n-dimensions down to 2-DMulti-dimensional scaling (MDS)Principal component analysis...

Other Techniques to reduce dataSampling – We only include every so many data cases or variablesAggregation – We cluster together many data cases or variables

What kinds of questions/tasks would you want such a technique to address?

Clusters of similar data casesUseless dimensionsDimensions similar to each other…


InformationVisualization 3. 3. FocusFocus + + ContextContext

Same idea as overview and detail, with one key difference:Typically, focus and context are combined into a single displayMimics our natural vision systems more closely

Possible methodsFilteringSelective aggregationHighlightingDistortion…


InformationVisualization 4. 4. TreesTrees andand HierarchiesHierarchies

HierarchiesData repository in which cases are related to subcasesCan be thought of as imposing an ordering in which cases are parents or ancestors of other cases

TreesHierarchies often represented as treesDirected, acyclic graph

Potential ProblemsTree might grow a lot along one particular branchHard to draw it well in view without knowing how it will branch


InformationVisualization 5. 5. InfoVisInfoVis SystemsSystems andand ToolkitsToolkits

Systems fall into three approachesMultivariate representation

Table Lens, EZChooser, SeeIt, InfoZoom

Collection of popular and new viewsAdvizor, Spotfire, InfoScope, ILOG Discovery, Polaris (Tableau)

Infrastructure with which to buildviews

InfoVis Toolkit, Prefuse, Piccolo

Each espouses a specific data representation


InformationVisualization 6. Interaction techniques6. Interaction techniques

Two main components in infovisRepresentationInteraction

Representation gets all the attentionInteraction is where the action isDix and Ellis (AVI ’98) propose

Highlighting and focusAccessing extra info – drill down and hyperlinksOverview and context – zooming and fisheyesSame representation, changing parametersLinking representations – temporal fusion

Keim’s taxonomy (TVCG ’02) includesProjectionFilteringZoomingDistortionLinking and brushing


InformationVisualization 7. 7. Taxonomies Taxonomies ofof Information Information VisualizationVisualization

Examine some existing taxonomies of the area and use them as a foundation of discussion

Do we agree with them?

E.g.


InformationVisualization 8. 8. VisualizationVisualization withwith Data Data ClusteringClustering

Force Directed Algorithm (Spring Algorithm)Hierarchical clustering methodsK-means…


InformationVisualization 9. Temporal 9. Temporal visualizationsvisualizations

Time Series DataFundamental chronological component to the data set

Data SetsEach data case is likely an event of some kindOne of the variables can be the date and time of the eventEx: Medicines taken, cities visited, stock prices, etc.

Time Series User TasksExamples

When was something greatest/least?Is there a pattern?Are two series similar?Do any of the series match a pattern?

Other TasksDoes data element exist at time t ?When does a data element exist?How long does a data element exist?How often does a data element occur?In what order do data elements appear?Do data elements exist together?…


InformationVisualization WhoWho’’s s doingdoing whatwhat??

x

Michael

Yannick

Patrick

Mehdi

Luca

Lorenzo

Jeannette

Gionatan

Daniele

x9. Temporal visualizations

x8. Visualization with Data Clustering

x7.Animation

x6. Interaction techniques

x5. InfoVis Systems and Toolkits

4. Trees and Hierarchies

x3. Focus + Context

x2. High-Dimensional Data Representations

x1. Visual Perception & Graphical Principles


InformationVisualization NextNext……

Vendredi 17 novembre 2006 14h15: Introduction au séminaire et choix des thèmes (Denis Lalanne)

Vendredi 15 décembre 2006 14h15: Présentation des recherches internes au groupe DIVA dans le domaine de la visualisation de l'information + tutoriel sur comment bien présenter une recherche scientifique:

FaericWorld (Maurizio Rigamonti)PIMViz (Florian Evequoz)NetSecurity (Enrico Bertini)« How to make a good scientific presentation »(Rolf Ingold)

Vendredi 12 janvier 2007 14h15: Présentation de chercheurs externes sur le thème de la visualisation de l'information:

Vendredi 26 janvier 2007 14h15: Présentation des étudiants du séminaire

Vendredi 2 février 2007 14h15: Présentations des étudiants du séminaire

http://diuf.unifr.ch/diva/web/index.php?option=com_content&task=view&id=84&Itemid=73



ExampleExample ofof companiescompaniesworkingworking in in InfovisInfovis

Macrofocus Infoscope

www.macrofocus.ch



AnotherAnother exampleexample ofof companycompany: : NetSecurityNetSecurity

NEXThink SA, Switzerland, project NetSecurity with UniFrNetSecurity high level goals:

Improve knowledge of system administratorsto react and take accurate and timely decisions

Help to focus on important thingsto permit efficient processing of very large informationto make important things apparent

information motivation - data explosion - unifr.ch · 2011. 9. 28. · infovis seminar 2006...

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