information motivation - data explosion - unifr.ch · 2011. 9. 28. · infovis seminar 2006...
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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
Denis Lalanne / UniFr 3InfoVis seminar 2006
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??
Denis Lalanne / UniFr 4InfoVis seminar 2006
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?
Denis Lalanne / UniFr 5InfoVis seminar 2006
InformationVisualization
Per capita income
Co
lleg
e d
eg
ree %
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InformationVisualization
VisualizationVisualization isis anan old old practicepractice ……
Denis Lalanne / UniFr 7InfoVis seminar 2006
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
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InformationVisualization
SemanticSemantic fisheyefisheyeViewView ofof thethe worldworld by a New by a New YorkerYorker
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InformationVisualization VisualizationVisualization isis aroundaround usus
... in Everyday tools
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InformationVisualization
InfoVisInfoVisisis
((ScreenScreen--BasedBased))
VisualizationVisualization + Interaction+ Interaction
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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
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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
Denis Lalanne / UniFr 13InfoVis seminar 2006
InformationVisualization
The The classicclassic definitiondefinition
Information visualization is the use of computer-supported, interactive, visual representations of abstract data to amplify cognition.
[Card, Mackinlay, Shneiderman]
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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
+
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InformationVisualization
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)
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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.
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InformationVisualization …… and interactionand interaction
e.g., link & brush in multiple views
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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)
Denis Lalanne / UniFr 19InfoVis seminar 2006
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
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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
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InformationVisualization
InfoVisInfoVis GalleryGallery ......
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InformationVisualization TreemapsTreemaps
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InformationVisualization SunburstSunburst
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InformationVisualization ParallelParallel CoordinatesCoordinates
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InformationVisualization GraphsGraphs
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InformationVisualization
Interaction Interaction ……
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InformationVisualization DynamicDynamic FilteringFiltering
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InformationVisualization LinkLink & & BrushBrush
EDV (exploratory data visualizer)
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InformationVisualization DistortionDistortion ((fisheyefisheye viewsviews))
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InformationVisualization
InfovisInfovis isis groundedgrounded on strong on strong perceptualperceptual principlesprinciples//studiesstudies ……
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InformationVisualization Attention Attention andand perceptionperception
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InformationVisualization DataData--featurefeature mappingmapping
get some data and give them a visual representation(data items -> visual items, data features -> visual features)
Denis Lalanne / UniFr 33InfoVis seminar 2006
InformationVisualization DataData--featurefeature mappingmapping
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InformationVisualization
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
Denis Lalanne / UniFr 35InfoVis seminar 2006
InformationVisualization
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
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InformationVisualization
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)
Denis Lalanne / UniFr 37InfoVis seminar 2006
InformationVisualization
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…
Denis Lalanne / UniFr 38InfoVis seminar 2006
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…
Denis Lalanne / UniFr 39InfoVis seminar 2006
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
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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
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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
Denis Lalanne / UniFr 42InfoVis seminar 2006
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.
Denis Lalanne / UniFr 43InfoVis seminar 2006
InformationVisualization 8. 8. VisualizationVisualization withwith Data Data ClusteringClustering
Force Directed Algorithm (Spring Algorithm)Hierarchical clustering methodsK-means…
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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?…
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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
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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
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InformationVisualization
ExampleExample ofof companiescompaniesworkingworking in in InfovisInfovis
Macrofocus Infoscope
www.macrofocus.ch
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InformationVisualization
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