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Scien&ficandLargeDataVisualiza&on
Introduc&ontoInforma&onVisualiza&on
MassimilianoCorsiniVisualCompu,ngLab,ISTI-CNR-Italy
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Nextlessons–Overview• Introduc&ontoInforma&onVisualiza&on• Mo&va&ons• DataTypes,GraphTypesandVisualPercep&on• Mul&dimensionalData• GraphDrawing• Prac&ce
– Visualiza&onontheWeb– Javascript,WebGL– D3.js
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Lesson14-15–IntrotoInfoVis• Introduc&onandmo&va&ons• Ingredientsofeffec&vevisualiza&on• Datatypes• Graphtypes• Visualpercep&on
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Informa,onVisualiza,on
• Informa,onvisualiza,onisthestudyof(interac,ve)visualrepresenta,onsofabstractdatatoreinforcehumancogni,on.[Wikipedia]
• Theuseofcomputer-supported,interac,ve,visualrepresenta,onsofabstractdatatoamplifycogni,on.[Cardetal.1999]
• Theuseofcomputergraphicsandinterac,ontoassisthumansinsolvingproblems.[Purchaseetal.2008]
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Informa,onVisualiza,on
• Thepurposeofinforma,onvisualiza,onistoamplifycogni,veperformance,notjusttocreateinteres,ngpictures.[Card2007]
• Infographicsisavisualtoolforcommunica,on,fortheunderstandingandfortheanalysis.[AlbertoCairo]
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Informa,onVisualiza,on
• DifferencefromScien4ficVisualiza4on:– Informa,onvisualiza,ontreatsalsoabstractdata(numericalandnon-numericaldata).
– Inscien,ficvisualiza,onspa,alrepresenta,onisgiven.
• DifferencefromVisualAnaly4cs:– InVisualAnaly,cstheaccentisonthereasoning/interac,onloop.
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InfoGraphics–Charts
From the Wall Street Journal
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InfoGraphics–Diagrams
Juan Colombata & Enzo Oliva – La Voz del Interior (Argentina)
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Mo,va,ons
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Mo,va,ons
Which country is close to its historical maximum ?
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Mo,va,ons
Easier to answer… Why ?
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Ra,onale
• Thehumanvisualsystem(HVS)isverygoodatiden,fiesandanalyzespaZerns.
• Wecanvisualizedatatomakeeasyforourbraintoanalyzethem,forexampletodocomparisons.
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Effec,veness
• Tobeeffec,ve,datavisualiza,onshouldbetakenintoaccountseveralfactors:– Datatype– Goal(func,on)– VisualPercep,onSystem
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Func,onalArt
• Func,ondoesnotdictatebutrestrictourchoices.
• Thisispar,cularlytrueforinfographics.
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Example–Comparisons
From “The functional art” by Alberto Cairo
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ClevelandandMcGill1984Lessaccurate
Lessaccurate
Adapted from “The functional art” by Alberto Cairo
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KeyIngredients
• Inthefollowingwefocuson:– Datatypes
• Onedimension,twodimensions,N-dimensions• Quan,ta,ve,Ordinal,Nominal
– Graphtypes– VisualPercep,on
• Wegivesomedesignguidelines,meby,me.
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Data
• Informa,onisobtainedfromdata(!)• Structured/Unstructured.• Generatedbysensors,bycomputers,byhumans,etc.
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VariableTypes• AccordingtoStevens(1946):– Nominal• Labels(e.g.apples,oranges,bananas)
– Ordinal• Toorderingthings(e.g.ranksofmovies)
– Interval• Intervalscaleofmeasurements(e.g.,meofdeparture-arrival)
– Ra,o• Measuresdefinedonara,oscale(e.g.themassofanobject)
S. S. Stevens, “On the theory of scales and measurements.”, Science, 103, pp. 677-680, 1946.
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VariableTypes
• Category– Steven’snominalclass(e.g.countrynames,typeofdisease)
• Ordinal– Labelsexpressingdegree(e.g.cold,hot,veryhot)– Ingeneral,encodedasintegerdata.
• Quan,ta,ve– Intervals,measures,etc.– Ingeneral,real-numbereddata.
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DataDimensions
• Commondimensions:– Univariate,bivariate,trivariate– Mul,-variate(N>3dimensions)
• Variablescanbedependentorindependent.• EachcaseisapointinaspacewithNdimension(datapoint).
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DataDimensions
• AsetofdatacanberepresentedbyatablewithNcolumns(oneforeachvariable).
Variable1 Variable2 Variable3
Data1
Data2
Data3
Data4
…
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DataRela,onships
• Atablecanbeusedtorepresentarela,onshipbetweendifferentdata.
UserId Gameid
Data1 Smith 023923
Data2 James 238548
Data3 Frank 385753
Data4 Powell 357352
…
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DataRela,onships
• 1-to-1• 1-to-many• Many-to-many• Rela,onshipsmayalsohaveaZributes
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Whatwewant..
• Asetofwellformedandinterconnectedtablesofdata.
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Whatwehave..
• Datadoesnotcomeintheformwewouldlike.
• Datamayhaveinconsistencies:– Corrupteddata– Missingdata– Severaldatamaybeequivalent(e.g.textfield“R&D”,“Research”,“ResearchandDevelopment”,“r*d”)
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DataProcessingPipeline
• Datadoesnotcomeintheformwewouldlike.
• Typicaldataprocessingpipeline(fromrawdatatocleanstructureddata):1. Collectdata.2. Datasimplifica,on(extractasubsetofinterest).3. Cleanandstructurethem.
• Anoutputofthepipelinecanbealsometadata.
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DataCollec,on
• Searchanddownloaddata.• Parsetext.• Convertbetweendifferentformats.– Examples:CVStoJSON,MySQLtoHTML,etc.
• Mergeheterogeneoussources.
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DataSimplifica,on
• Filter/Selec,on– Removeunwanteddata– Removeinvaliddata(nullvalues)
• Aggrega,on– Collapseseveraldatapointsintoasingleone– Replacesomevalueswithminimum,maximum,average,total,etc.
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DataProcessingPipeline
• Typicallyperformedautoma,callyusingscripts.
• Human-guideddatatransforma,onsispossible(throughmacro-opera,ons)– Useappropriatetools(e.g.OpenRefine-h?p://openrefine.org)
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Metadata
• Datawhichdescribesthedata– Roleofvariables– Typeofvariables– Constraints– Dependencies
• Collec,onandprocessingopera,onscanbealsodescribed.
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HowtoPresentData?
• Howtopresentdatagraphically?– Toallowvisualanalysis– TohighlightpaZerns– Toanswersomespecificques,ons– Etc.
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Quan,ta,veValues
• Wehavejustmen,onedtheworkbyCleveland&McGill(1984)– Posi,on– Length– Angle/slope– Area– Volume– Colorsatura,on/shading
PerceptualAccuracy
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From “Data Visualization” Course by John C. Hart, for Coursera, 2015.
QUANTITATIVE ORDINAL NOMINAL
Posi,on Posi,on Posi,on
Length Density Hue
Angle Satura,on Texture
Slope Hue Connec,on
Area Texture Containment
Volume Connec,on Density
Density Containment Satura,on
Satura,on Length Shape
Hue Angle Length
Slope Angle
Area Slope
Volume Area
Volume
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TablevsGraphs
• Presenttablesdirectlyispreferredwhen:– Fewdatapoints– Precisevaluesareimportant
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UnivariateData
• Fewinteres,ngsolu,ons.• Sta,s,caldescrip,on:– Mean,median,standarddevia,on,quar,les.
• Warning!(somedatathatappearstobeunivariateareactuallybivariate).
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Stemplots
• Alsocalledstemandleafplot.
• Usedtodisplayquan,ta,vedata,generallyfromsmalldatasets(50orfewerobserva,ons).
• Easytoprint.• Easytoread.
Figure from Data Visualization Catalogue (http://datavizcatalogue.com)
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Stemplots
Figure from Data Visualization Catalogue (http://datavizcatalogue.com)
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BoxandWhiskerPlots• BoxandWhiskerPlot(orBoxPlot)isaconvenientwayofvisuallydisplayingadatadistribu,onthroughtheirquar,les.
• Advantages:– Keyvalues(average,median,25th
percen,le,etc.)– Ifthereareanyoutliersandwhattheir
valuesare.– Ifthedataisskewedandinwhat
direc,on.
Figure from Data Visualization Catalogue (http://datavizcatalogue.com)
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BoxandWhiskerPlots
Figure from Data Visualization Catalogue (http://datavizcatalogue.com)
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LineCharts/LineGraphs
• InventedbytheScotshengineerandsta,s,cianWilliamPlayfair(1759-1823)
• Twoquan,ta,vevariables,typically:– Xà,meorintervals,Yany
• LineindicatesthattherearealsointermediatevaluesFigure from Data Visualization Catalogue (http://datavizcatalogue.com)
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BarCharts
• BivariateData– Onenominalvariable(typicallyindependent)
– Onequan,ta,vevariable(typicallydependentvariable)
• Horizontal/Ver,calbars• Donotconfusewithhistograms.
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BarCharts
3D?!Notaverygoodidea..
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Histograms
• BivariateData– Oneindependentandonedependentvariable
– Thefirstvariableisquan,zedinintervals(bins)
Figure from Data Visualization Catalogue (http://datavizcatalogue.com)
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PieCharts
• BivariateData– Oneindependentandonedependentvariable
• Goodforaquickvisualcheck.
• Notgoodfor:– Manyvalues.– Accuratecomparisons.
Figure from Data Visualization Catalogue (http://datavizcatalogue.com)
![Page 46: Scienfic and Large Data Visualizaon Introduc&on to ...vcg.isti.cnr.it/~cignoni/SciViz1718/SciViz_10_Intro_InfoVis.pdf · Data Processing Pipeline • Data does not come in the form](https://reader035.vdocument.in/reader035/viewer/2022071018/5fd1b6f974274e3a855d7f5c/html5/thumbnails/46.jpg)
Figure from Data Visualization Catalogue (http://datavizcatalogue.com)
![Page 47: Scienfic and Large Data Visualizaon Introduc&on to ...vcg.isti.cnr.it/~cignoni/SciViz1718/SciViz_10_Intro_InfoVis.pdf · Data Processing Pipeline • Data does not come in the form](https://reader035.vdocument.in/reader035/viewer/2022071018/5fd1b6f974274e3a855d7f5c/html5/thumbnails/47.jpg)
PieCharts
3D?!Notmorereadable.
![Page 48: Scienfic and Large Data Visualizaon Introduc&on to ...vcg.isti.cnr.it/~cignoni/SciViz1718/SciViz_10_Intro_InfoVis.pdf · Data Processing Pipeline • Data does not come in the form](https://reader035.vdocument.in/reader035/viewer/2022071018/5fd1b6f974274e3a855d7f5c/html5/thumbnails/48.jpg)
Figure by Luiz Salomão.
![Page 49: Scienfic and Large Data Visualizaon Introduc&on to ...vcg.isti.cnr.it/~cignoni/SciViz1718/SciViz_10_Intro_InfoVis.pdf · Data Processing Pipeline • Data does not come in the form](https://reader035.vdocument.in/reader035/viewer/2022071018/5fd1b6f974274e3a855d7f5c/html5/thumbnails/49.jpg)
• Essen,ally,apiechartwiththecenterareacutout.
• Allowstofocusmoreonarclengthinsteadofcomparingthepropor,onbetweenslices.
• Morespaceefficient.
Figure from Data Visualization Catalogue (http://datavizcatalogue.com)
DonutCharts
![Page 50: Scienfic and Large Data Visualizaon Introduc&on to ...vcg.isti.cnr.it/~cignoni/SciViz1718/SciViz_10_Intro_InfoVis.pdf · Data Processing Pipeline • Data does not come in the form](https://reader035.vdocument.in/reader035/viewer/2022071018/5fd1b6f974274e3a855d7f5c/html5/thumbnails/50.jpg)
SunburstCharts• Toshowshierarchythroughaseriesofrings.Eachringcorrespondstoalevelinthehierarchy.
• Hierarchymovingoutwardsfromthecenter.
• Colourcanbeusedtohighlighthierarchalgroupingsorspecificcategories.Figure from Data Visualization Catalogue (http://datavizcatalogue.com)
![Page 51: Scienfic and Large Data Visualizaon Introduc&on to ...vcg.isti.cnr.it/~cignoni/SciViz1718/SciViz_10_Intro_InfoVis.pdf · Data Processing Pipeline • Data does not come in the form](https://reader035.vdocument.in/reader035/viewer/2022071018/5fd1b6f974274e3a855d7f5c/html5/thumbnails/51.jpg)
SunburstCharts
Produces by Space Radar app (https://github.com/zz85/space-radar)
![Page 52: Scienfic and Large Data Visualizaon Introduc&on to ...vcg.isti.cnr.it/~cignoni/SciViz1718/SciViz_10_Intro_InfoVis.pdf · Data Processing Pipeline • Data does not come in the form](https://reader035.vdocument.in/reader035/viewer/2022071018/5fd1b6f974274e3a855d7f5c/html5/thumbnails/52.jpg)
ScaZerPlots
• BivariateData– Twoindependentvariables
• Goodtoiden,fyrela,onships,outliersandclusters.
Variable1
Variable2
![Page 53: Scienfic and Large Data Visualizaon Introduc&on to ...vcg.isti.cnr.it/~cignoni/SciViz1718/SciViz_10_Intro_InfoVis.pdf · Data Processing Pipeline • Data does not come in the form](https://reader035.vdocument.in/reader035/viewer/2022071018/5fd1b6f974274e3a855d7f5c/html5/thumbnails/53.jpg)
ScaZerPlots
Variable1
Variable2
Outliers
HighDensity
Variable1
Variable2Clusters
![Page 54: Scienfic and Large Data Visualizaon Introduc&on to ...vcg.isti.cnr.it/~cignoni/SciViz1718/SciViz_10_Intro_InfoVis.pdf · Data Processing Pipeline • Data does not come in the form](https://reader035.vdocument.in/reader035/viewer/2022071018/5fd1b6f974274e3a855d7f5c/html5/thumbnails/54.jpg)
SurfaceGraphs
• TrivariateData– Threecon,nuousvariables
– Twoindependentandonedependent
![Page 55: Scienfic and Large Data Visualizaon Introduc&on to ...vcg.isti.cnr.it/~cignoni/SciViz1718/SciViz_10_Intro_InfoVis.pdf · Data Processing Pipeline • Data does not come in the form](https://reader035.vdocument.in/reader035/viewer/2022071018/5fd1b6f974274e3a855d7f5c/html5/thumbnails/55.jpg)
SurfaceGraphs
• Colormaybeassociatedtothedependentvariable.
![Page 56: Scienfic and Large Data Visualizaon Introduc&on to ...vcg.isti.cnr.it/~cignoni/SciViz1718/SciViz_10_Intro_InfoVis.pdf · Data Processing Pipeline • Data does not come in the form](https://reader035.vdocument.in/reader035/viewer/2022071018/5fd1b6f974274e3a855d7f5c/html5/thumbnails/56.jpg)
SurfaceGraphs
• Levelcurvescanbealsoused.
![Page 57: Scienfic and Large Data Visualizaon Introduc&on to ...vcg.isti.cnr.it/~cignoni/SciViz1718/SciViz_10_Intro_InfoVis.pdf · Data Processing Pipeline • Data does not come in the form](https://reader035.vdocument.in/reader035/viewer/2022071018/5fd1b6f974274e3a855d7f5c/html5/thumbnails/57.jpg)
3DScaZerPlots
• TrivariateData– Threequan,ta,vevariables
• Sameconceptof2DScaZerPlot
![Page 58: Scienfic and Large Data Visualizaon Introduc&on to ...vcg.isti.cnr.it/~cignoni/SciViz1718/SciViz_10_Intro_InfoVis.pdf · Data Processing Pipeline • Data does not come in the form](https://reader035.vdocument.in/reader035/viewer/2022071018/5fd1b6f974274e3a855d7f5c/html5/thumbnails/58.jpg)
ColoredScaZerPlots
• TrivariateData• Colorcanencodeavariableoracategory
![Page 59: Scienfic and Large Data Visualizaon Introduc&on to ...vcg.isti.cnr.it/~cignoni/SciViz1718/SciViz_10_Intro_InfoVis.pdf · Data Processing Pipeline • Data does not come in the form](https://reader035.vdocument.in/reader035/viewer/2022071018/5fd1b6f974274e3a855d7f5c/html5/thumbnails/59.jpg)
BubbleCharts
• TrivariateData– Threequan,ta,vevariables
• Donotallowforaccuratecomparison.
• Colorscanbeusedtoshowdifferentcategories.
![Page 60: Scienfic and Large Data Visualizaon Introduc&on to ...vcg.isti.cnr.it/~cignoni/SciViz1718/SciViz_10_Intro_InfoVis.pdf · Data Processing Pipeline • Data does not come in the form](https://reader035.vdocument.in/reader035/viewer/2022071018/5fd1b6f974274e3a855d7f5c/html5/thumbnails/60.jpg)
BubbleCharts
Figure from http://gapminder.com
![Page 61: Scienfic and Large Data Visualizaon Introduc&on to ...vcg.isti.cnr.it/~cignoni/SciViz1718/SciViz_10_Intro_InfoVis.pdf · Data Processing Pipeline • Data does not come in the form](https://reader035.vdocument.in/reader035/viewer/2022071018/5fd1b6f974274e3a855d7f5c/html5/thumbnails/61.jpg)
ChloroplethMap
• BivariateData– Quan,ta,vevalueoverageographicalareas/regions
• Dataarecoloured,shadedorpaZernedindifferentways.
• Goodforanoverview,notforaccuratecomparison.• Smallareascanbeunderemphasized.
![Page 62: Scienfic and Large Data Visualizaon Introduc&on to ...vcg.isti.cnr.it/~cignoni/SciViz1718/SciViz_10_Intro_InfoVis.pdf · Data Processing Pipeline • Data does not come in the form](https://reader035.vdocument.in/reader035/viewer/2022071018/5fd1b6f974274e3a855d7f5c/html5/thumbnails/62.jpg)
WordCloud
• WordCloudsdisplayshowfrequentlywordsappearinagivenbodyoftext,bymakingthesizeofeachwordpropor4onaltoitsfrequency.
• Arrangementandcolorcanvaryalot.• WordCloudscanbeusedtocomparetwobodiesoftextortogiveaquickideaofrepea,ngkeywords(e.g.usedbyresearcherstosummarizethecontentoftheirpapers).
![Page 63: Scienfic and Large Data Visualizaon Introduc&on to ...vcg.isti.cnr.it/~cignoni/SciViz1718/SciViz_10_Intro_InfoVis.pdf · Data Processing Pipeline • Data does not come in the form](https://reader035.vdocument.in/reader035/viewer/2022071018/5fd1b6f974274e3a855d7f5c/html5/thumbnails/63.jpg)
WordClouds
• Disadvantages:– Longwordsareemphasizedovershortwords.– WordswhoseleZerscontainmanyascendersanddescendersmayreceivemoreaZen,on.
– Noaccuracycomparison,mainlyusedforaesthe,creasons.
![Page 64: Scienfic and Large Data Visualizaon Introduc&on to ...vcg.isti.cnr.it/~cignoni/SciViz1718/SciViz_10_Intro_InfoVis.pdf · Data Processing Pipeline • Data does not come in the form](https://reader035.vdocument.in/reader035/viewer/2022071018/5fd1b6f974274e3a855d7f5c/html5/thumbnails/64.jpg)
WordClouds(example)
Produces by www.jasondavies.com/wordcloud.
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WordClouds(example)
Produces by www.jasondavies.com/wordcloud.
![Page 66: Scienfic and Large Data Visualizaon Introduc&on to ...vcg.isti.cnr.it/~cignoni/SciViz1718/SciViz_10_Intro_InfoVis.pdf · Data Processing Pipeline • Data does not come in the form](https://reader035.vdocument.in/reader035/viewer/2022071018/5fd1b6f974274e3a855d7f5c/html5/thumbnails/66.jpg)
WordClouds(example)
From www.wordclouds.com .
![Page 67: Scienfic and Large Data Visualizaon Introduc&on to ...vcg.isti.cnr.it/~cignoni/SciViz1718/SciViz_10_Intro_InfoVis.pdf · Data Processing Pipeline • Data does not come in the form](https://reader035.vdocument.in/reader035/viewer/2022071018/5fd1b6f974274e3a855d7f5c/html5/thumbnails/67.jpg)
Summary
• Abriefintroduc,ononInforma,onVisualiza,onhasbeengiven.
• Informa,oncomingfromdata.Datathatshouldbecollectedandprocessedproperly.
• Aquickpanoramicofgraphtypeshasbeengiven.
![Page 68: Scienfic and Large Data Visualizaon Introduc&on to ...vcg.isti.cnr.it/~cignoni/SciViz1718/SciViz_10_Intro_InfoVis.pdf · Data Processing Pipeline • Data does not come in the form](https://reader035.vdocument.in/reader035/viewer/2022071018/5fd1b6f974274e3a855d7f5c/html5/thumbnails/68.jpg)
Ques&ons?