changing the size attributesince they use a layou t that remains stable. Only some treemaps described in [3] use la yout algorithms that are not geometrically ...
Interactive Information Visualization oaM f illion Jean-DanielFekete*
Items
Catherine Plaisant
HumanComputerInteractionLaboratory University oMaryland f College Park,MD20742 +1(301)4052768 http://www.cs.umd.edu/hcil {fekete,plaisant}@cs.umd.edu
ABSTRACT Existinginformationvisualizationtechniquesareu tothedisplayoaffewthousanditems.Thisarticl interactivetechniquescapableofhandlingamillio (effectivelyvisibleandmanageableonscreen). We useohardware-based f techniquesavailablewithnew cards,aswellasnewanimationtechniquesandnongraphicalfeaturessuchastereovision s andoverlap Thesetechniqueshavebeenappliedtotwopopulari visualizations:treemapsandscatterplotdiagrams; generic enoughtobaeppliedtother2Drepresenta
suallylimited de escribesnew nitems evaluatethe ergraphics standard count. nformation butare tionsaswell.
Keywords I.3.6MethodologyandTechniques;H.5.2UserInterf Picture/Image Generation.
aces;I.3.3
1.Introduction Informationvisualizationisaresearchdomainaimi ngat supportingdiscoveryandanalysisofdatathroughv isual exploration. MadepopularbyEdwardRTufte’s . boo ks[20],its principleistomaptheattributesoaf nabstractd atastructureto visualattributessuchaC s artesianposition,color andsize,andto display the mapping. This is in contrast with scien tific visualization,whichdealswithdatathatusuallyh asanintrinsic representation.
supervisedanalysis,butmaximizingthenumberoif foragivendisplaysizeisalsoachallengeforde visualizationsonsmalldeviceslike PDAs. Inthisarticle,wedescribenewtechniquestovisu itemsusing standarddisplays. These techniquesre
temsvisible signersof alizeamillion ly on:
•
hardwareaccelerationtoachievethespeedrequired interactionandanimation;
•
non-standardvisualattributessuchasstereovision synthetic overlapcounttoenhance visualization;
•
animation and interaction while replaying recorded visualizationconfigurations(views)usingtimemul tiplexing techniquestoanalyzethedataacrossseveralviews and mappingswithoutlosing context.
Wehaveappliedthesetechniquestotreemaps(Figur scatterplots.Treemapsarerepresentativeofspac visualizations whereas scatter plots are representa visualizationswithoverlapping,butthetechniques herearegeneralenoughtobeappliedtootherkind visualizations.
for or
e 1)and efilling tive of wedescribe sof2D
Popularmappingsexistfor laargerangeodata f st ructuressuchas tables,trees,graphsaw s ellasmorespecializedo nes.Duringthe lastdecade,dozensofnewvisualizationtechniques were invented,suchastreemaps[12]orConeTrees[17]. Most techniqueshavestrong a interactioncomponentallo wingusersto rapidlyexplorethedata[1,14]. Commercialproduc tsarenow availableandsuccessfullyusedinawidearrayof applications domainssuchaoil s productionodrug r discovery [6 ]. Yet,littleisknownaboutthelimitsoifnformatio nvisualization techniquesintermofscalability. Currentsystems tendtoavoid theproblem by relying onaggregationosampling r t echniquesthat 4 5 limitthe number of visible itemstoabout10 andoccasionally 10 atthecostoifnteraction. Ourgoalistodisplay andmanagea millionitems.By item,wemean atomicobjectdisplayedasa distinguishable contiguous area using one visualiza tion technique. Forsystemsdisplayingthesameobjectseveralt imes accordingtodifferentattributes,wecounteachin stanceasa differentitem. Managementoftheitemsinvolvesm aintaining continuousinteractionandsmoothanimation. Scalabilityissuesareimportantininformationvis becauseoftheproliferationoflargedatasetsreq *alsovisitingfromEcole desMinesde Nantes,Fran
ualization uiringhuman-
Figure 1This : treemap give an s overviewofthe 970,000files oflaarge file system on 1600x1200 a display.The size of each rectangle idetermined s bythe file’ssize;co lor representsfile type.Deeplynested directoriesapp ear darker.
2.PreviousWork Visualizingonemillionitemsisaproblemofvisua perceptionandinteraction.
lization,
ce 1
2.1VisualizationoLarge f Data Sets Existinginformationvisualizationtechniquesareu suallylimited tothedisplayofafewthousanditemsor avoidtheproblemof visualizinglargenumberofitemsbyusingaggregat ion,sampling andextracting,or by notmanaging occlusionandov erlapping. Amongthepopulartechniquesistheuseofscatter connectedtointeractivecontrolssuchaisnDynami Scatterplotsvisualizemultidimensionaldatabyma dimensionstotheXandYcoordinatesandmappingo dimensionstovisualattributessuchascolor,widt orientation,intensityosrhape. Whenaugmentingt visibledatainscatterplots,overlappingcannotb notmanagedaall tby currentsystems.
plots Q c ueries[1]. ppingtwo ther h,height, henumberof aevoidedandis
Avoiding overlaps,space-filling techniquessuchas VisDB[13]andSeeSoft[8],offerahighdensityof Theyusespeciallayoutalgorithmstofillupallt thusrequiringredrawingallthesepixelswhenavi parameterchanges. Currentimplementationsothes f limitedmostlybytheredrawtime,andtoasmaller timetocompute the layout. VisDBhandles50,000i 50,000linesocf odeandMicrosoftNetScanproject thousandsof newsgroupsin treemap. a
treemaps[12], information. hescreenpixels, sualization seystemsare extentbythe tems;SeeSoft canrender
Treemapsarevisualizationtechniques fortrees.Theywere introducedbyShneiderman[12]andseveralvariatio nshavebeen createdwhichimprovetheirreadability. Theiniti al“sliceand dice”algorithmissimple:itusestheentirescree ntorepresentthe treerootanditschildren.Eachchildoftheroot isgivena horizontalorverticalstripofsizeproportionalt othecumulative sizeofitsdescendents.Theprocessisrepeatedr ecursively, flippinghorizontalandverticalstripsateachhie rarchylevel. Somevariantstrytoavoidlongthinrectanglesand allocatestrips containing sub-treesassquare apossible s [3]. HybridtechniquessuchasMihalisinAssociatesSyst emcan 4 8 visualizedatasetsrangingfrom10 to 10 data points[13]along withseveraldimensionsand“measures”;however,th eyrelyon sampling,limitingthenumberofvisibleitemston umbersnot specifiedbytheauthor.Jerdingetal.“Informati onMural” technique[11]iasgoodexampleosfystemdisplayi nghundreds of thousanditemsby relying onaggregation. Current visualizationsystemsarelimitedtoabout10,000i tems partlybecausecontrolpanels,labelsandmarginsw astetoomany pixels,thedatastructuresarenotoptimizedfors peed,andthey useslowgraphicslibraries,whichbringstheinter actiontovery a slowmotionwhendealingwithmorethan10,000item s(for exampleSpotfirecanloadmorethan10,000itemsbu tthe interactionsuffersenormouslyb). Addressingthose threeissuesis necessary –butnotsufficient –tohandle million a items.
2.2Perception To beeffective,visualizationtechniquesshouldrely asmuchas possibleonpreattentivegraphicalfeatures[10][20 ].These featuresareprocessedbythelowlevelvisualsyst emandcanbe recognized“ataglance”withouteffort.Anexampl eof preattentiveprocessingconsistsinspottingreddo tsamong severalbluedots. Itdoesnottakeanyeffortto seewhetherthere areoneosreveralreddotsanditcanbedoneinl essthan200 millisecondsiftheregionissmallenoughtobese eninone glimpse (more experiments can be found at http://www.csc.ncsu.edu/faculty/healey/PP/PP.html). Without
pre-attentiveprocessing,spottingafeaturerequir thenumberof eaturesandwillnotscalewelltod millionsoiftems. Anexampleonon-preattentive f reading.Findinganameinanon-sortedlistrequi proportionaltothenumberoflabels(ormorewhen track ogives r up.)
estimelinearto isplaysof featureitsext resatime theuserloses
Thelistofvisualfeaturesavailabletovisualize abstract informationislong,butonlyasmallsetcanbeus edina preattentiveway.Healeyliststhemin[10]as:li ne(blob) orientation,length,width,size,curvature,number t,erminators, intersection,closure,color(hue),intensity,flic ker,directionof motion,binocularluster,stereoscopicdepth,3Dde pthcuesand lightingdirection. Furthermore,thislistonlyme ansthatinsome controlled configuration, these features can be pro cessed preattentively,notthattheyarealwaysprocessed thisway. For example,Healeyhasconductedexperimentsthatshow thatonly fivetosevendifferentwell-chosencolorscanbep rocessed preattentively. Whentryingtousemorecolors,th errorrateand timerequiredto searchcoloreditemsincreasedsubstantiallyand searchtimebecomelinear. Inaddition,combining twoormore preattentivefeaturescancreateinterferencessoi npracticeonly twoothree r featurescanbuesedtogether.
2.3Interactive Techniques In1994,AhlbergandShneiderman[1]definedthest informationseekingas:startwithanoverviewoft zoominonitemsofinterestandfilteroutuninter thendetailsondemands. Increasingthenumberof permitsricheroverviewstobepresented. Theyals theterm“dynamicqueries”todescribeinteractive interactivelyspecifyingsearchqueriesindataset is:
epsovisual f hedataset, estingitems, visibleitems ointroduced methodsfor The s. definition
•
visualpresentationofthequery’scomponents(with buttons andrange sliders); • visualpresentationoresults; f • rapid[around100ms],incremental,andreversiblec ontrolof the query; • selectionbypointing,nottyping;andimmediatean d continuousfeedback. Couplingvisualizationwithdynamicquerieshasinc reased the effectivenessofvisualizationtechniquesbyallowi ngusercontrolledtemporalresearchonthevisualizeddata .Current systemsdynamicallyfiltervisualizeddatathrough slidersand buttonsupto10,000items.Abovethat,therefresh ratebecomes unacceptable.Anoptimizationtechniquedeveloped byTanin [19]demonstratesdynamicquerieswith100,000item sbyprecomputingthevisibleitemsforeachreachableposi tionofthe slider bar butthistechnique waslimitedbtyhe re display time. Toshowmoreitemsordimensions,allthevisualiza tion techniquescanusespacemultiplexing,timemultipl exing, overlapping, or space deformation techniques. Spac e multiplexingtechniquesdisplaytwoormorevisuali zation configurationsonthesamescreen,usingfewerpixe lsforeach configuration. Thisiismpracticalwhenattempting tovisualizea millionitemsbecausespaceiaslreadyscarcetobe ginwith. Time multiplexingtechniquesshoweachconfigurationsuc cessively, eitheratraegularpace(animation),orbyusingc ontrolpanels,or byfollowinginteractivemethodssuchads ynamicqu eries. Each configurationshouldappearinlessthan100mstom aintain
2
continuousinteraction. Overlappingtechniquessuc hasMagic Lenses[4]andExcentricLabels[9]show transientinformation overthevisualizationandcanbeusedeffectively ondensedata. Severalinteractivetechniqueshavebeendesignedt oenhancethe interactionforvisualizationandsparingscreenre alestate. Seethroughtools[4]areinteractiveenhancementstoM agiclenses; theyfilterthevisualizationin-placeusingtransp arencyor overlapping. ExtensionsoPie-Menus f [5]suchaC s ontrolMenus [18]canbeusedtooverlaycontrolswithoutusing permanent screenrealestate. Space-deformationtechniquessuchas[14]aresampl aggregationtechniquesthattrytoshowdetailsin interestandonly show “importantfeatures” osamp r
ingor thezonesof leselsewhere.
Aggregationsprovidepowerfulsummariesbutcansom etimehide phenomenaonlyvisibleat finergrain. Forexample,U a Smapof mortalitydataathestatelevelwillhidelocalo utliersandeven errorsinthedata. Thisarticlefocusesontechni questhatpush back further the needfor aggregationandsampling.
hardwbarereducestheloadothe f mainCPU(e.g.al donethere)andoffersmanynon-standardgraphicsa we usedtoenhance the visualizationodense f data
rendering l is ttributesthat sets.
4.1Appropriate VisualAttributes Weonlyusequadrilateralstorepresentitemsbecau se,inadense configuration,theperceptionofshapesisubject tointerference. Asitemdensityincreasesshapesoverlapandappear tomerge. Also,contrarytoseveralexistingsystems,wedon’ outline t items usingaone-pixelblackline,whichwastestwoline sandtwo columnsofpixelsandrequiressendingthecoordina testwice. Instead,weuseslightlyshadedquadrilateralssot hattheyremain distinguishablewhentiledorstacked(seefigure2 The ). other visualattributesweusearesaturatedcolors(for categoricalor numeral attributes), intensity (for numeral attribu tes), quadrilateralsizesandpositioninscatter plots.
3.TechnicalConstraints Todisplayonemillionofitems,weneedtoaddress resolutionandspeedissues.
screen
Screendefinitionandresolution: Currenthighendscreens displayaround2millionpixels(1600x1200)aatre solutionof 150dpi,withthenewestscreenscapableodisplayi f ng aroundten millionpixelsa200dpi. t Sodisplaying1million ofitemsshould notbeproblem a ifeachitemfitsin2to10pixel isnaverage,not countingtheoverlaps. Screensovr ideoprojection cs anbetiled toincreasethenumberofpixelsavailable,virtual lyremovingany limitation,butincreasingthephysicalsizeothe f displayrequires moreheadmovementsandslowsdownperception. An alternative istoincreasescreenresolution.Thelimitwould behuman perception:theoreticallyaround24millionpixels, practically around10million,andheadobody r movementscans tillbeused togetclosertoorfartherawayfromthedisplay. Theonly alternativetothesemovementsistimemultiplexing ,through constantanimationorinteractivecontrolsuch asascrollbarsfor panningandzooming,whichareevenlesseffective than movements. Wehavefocusedourresearchon1600x1200displays theyarewidespreadandwellmanagedbycurrentacc graphicsboards.
because elerated
Redisplaytime when : timemultiplexingius sed,theredisplayrat e shouldbm e aintainedaround10framespersecond.I one f million itemshavetobedisplayedathis t rate,usingacce leratedgraphics cardsistheonlyoptionandopensthedoortovisu alization enhancements. Commongraphicscardscandisplayar ound15 milliontrianglespersecondatbestsomaintaining anacceptable refreshrateforonemillionitemsdemandsspecial techniquesand more expensive cardsor waiting for the nextgenera tionocards. f Forourwork,wehaveused NVidiaGeForce3and3DlabWildcat hardwareacceleratedboardswith2GHzand1.7GHz Pe ntium PCs andthe OpenGL API[22]withcode writteninC++.
4.ReachingOne MillionItems Toaddressthetechnicalissuesinvolvedinvisuali interactingwithonemillionitems,wehavedesigne techniquesrelyingonacceleratedgraphicshardware high-densityinteractivevisualization. Theaccele
zingand dnovel toprovide ratedgraphics
Figure 2Smooth : shaded rectangleshelp distinguish dense visualizations(detailsofscatter plots) Wedescribeeachquadvertexwithfourvalues:X,Y andYarepositions. TheZcoordinateim s ainlyus stereovision:wetiltthequadssothattheupperl closertothecameraandthelowerrightcornerfar fog function changes theirintensityand smooth sha interpolatesitacrossthe rectangle. InsteadofsendingRGBAcolorsforeachquad,weus dimensionaltextureindices. Thetexturecanconta colorsforcategoricalattributes:onecolorperca alsocontainstartingandendingcolorforcontinuo attributes. Wethenrelyonhardwarelinearfilter in-betweencolorsasshowninfigure2. Wealsous transformstomapfromabstractattributevaluesto avoiding allcolor computationonthe CPUside. Moreattributescanbeusedifrequired.Forinsta treemaps,wealsousethefogfunctiontofadethe whenitemsaredeeperinthetreebyassigningthe attributetotheZcoordinateotfhequadrilaterals Morecontrolcouldbeobtainedbyusingatwodimen textureandassigningtheUtexturecoordinate(als availableviatheacceleratedgraphicshardware)to attributeandtheVcoordinatetoanotherone. How coloringschemewhe avefoundtobeffectiveusing featureswithtwoattributeswasassigning satura a toone attribute andvarying the brightnesswithth
itemsin Zand , X S. edforfogand eftcorneris theraway;the ding eoneinasetof tegory. Itcan usvalued ingtogenerate etexture colorvalues, nce,for colorstoblack treedepth (seeFigure1). sional oeasily oneabstract ever,theonly pre-attentive tedsetofcolors oether.
4.1.1Syntheticoverlapattribute Whensendingdatatothegraphicshardware,thecou overlappingitemscanbecalculatedusingthestenc Displayingthecontentofthestencilbufferasint overlappingcount(seefigure3This .) synthetica importantforvisualizationtechniquesthatcannot suchascatterplotsandparallelcoordinates. Ev ofitems,thedistributiontendstobesparsewith densitythatarehardtosee. Transparencyisusef
ntof ilbuffer. ensityshowsthe ttributeivs ery avoidoverlaps enwithhundreds areasohf igh ulwhenupto 3
allowsquickcomparisonstobemadeusingretinape rsistency. Thistechniqueiswidelyusedinastronomytotrack variations overtime(called blinking.)Byflippingbackandforthand movingthefocusofattention,twoviewscanbecom paredin seconds.
Figure 3Scatter : plotof1million itemson the le countson the right. The brightrectangle in the u corner revealthatmanyitemsare overlapping. The aligned,and unnoticeable in the leftview. fiveitemsoverlap,butwithonemillionitems,hun overlapping itemsare common. Theoverlapcountcanalsobemappedtocolor,oru thedisplayandrevealsapecificlayerofitems. similartodynamicqueriesonstandardattributesb onthestencilbufferforrejectingfragmentsabove specifiednumber of overlaps.
ftand overlap pper right yare dredsof sedtofilter Thistechniqueis utinsteadrelies orbelowa
4.1.2Transparencyand Stereovision Usinganacceleratedgraphicscardprovidesgraphic attributesnot availablewithtraditionalgraphicsAPIs. Wehave experimented withtransparencyandstereovision. Transparencyi sbeneficial withoverlappeditemsbutisnotsufficientbyitse lftounderstand thenumberofoverlaps.Furthermore,byblending colors,it interferes with its preattentive processing. There fore, transparencyios nlyusefulwhenitcanbevariedi nteractivelyto revealoverlapsanddensity ooverlapping f items.
Moreviewscanbfelippedthrough,similartoflipp oftraditionalanimators,tohelpdiscovertrendsa acrossmultipleviews. Thistechniqueonlyrequire timebelow300msandarefreshtimebelow50ms,onl achievable withdouble buffering.
ingtechniques ndoutliers saredisplay y
4.2.2Interpolation ogeometrical f attributes Whenthegeometryismodifiedbetweentwoviews,it difficulttoimpossibletounderstandrelationships withflipping,evenwithonly few a items[15]. Wehaveimplementedset a ofinterpolation thetransformationsfromoneviewtotheothersot followsetsoiftemsandunderstandpatternsocf ha views. Becausethechangescouldbceomplex,users subsetoiftemsandweonlyanimatethoseitems.Th lastsoneotwo r secondandcanbreeplayedback an Aslidercanalsobeusedtomanuallycontrolthei betweenviewstofacilitate understanding oitem f m
becomes betweenviews techniquestoanimate hattheeyecan ngebetween canselecta eanimation dforthawill. t nterpolation ovements.
Stereovisionhardwareisnowavailableforallthe standard graphicscardsforlessthan$100throughshutterg lasses. Stereovisionispreattentive,butthereagain,over lapsinterfere withitandcannotbeavoidedatall,evenwithspa ce-filling visualizationtechniques,sincestereovisionrequir esperspective a projection thatintroducesocclusionandthereforeoverlaps. Like transparency,wehavefoundstereovisionmostlyuse fulfor transientinspections.
4.2AnimationandInteraction Exploringalargedatasetwithouta-prioriknowled requirestryingseveralmappingsofdataattributes attributes. A specialproblem happenswhenchangin timemultiplexing:thewholelayoutofthescreenc usercannottellwheresetsorfegionsoifnterest viewhavegoneonthenewone. Alongtimemaybe understandtherelationshipbetweenitemsvisualize viewandinthepreviousone. Thisproblem isnot bycurrentsystems. Whenthenumberofitemsism multiplexingpossible,twoormoreviewscouldbed togetherusing“snaptogether”techniques[16]and linkingtechniques[7]canbeusedtoexplorethet viewsbyhighlightingitemsinmultipleviews;but optioninour case. Wefirstdescribethesimplecaseonon-geometrica f thenthe generalcase.
getypically tovisual vgiewsusing hangesandthe intheoriginal requiredto dinthenew addressedaall t allandspace isplayed brushingand ightlycoupled thisins otan changes l and
4.2.1View Flipping Whenthepositionsotfhedataitemsarepreserved –i.e.when onlychangingcolorsosrtackingorder –flippingbetweenviews
Figure 4The : animation othe f treemap (with squa a layout on the leftor slice&dice layouton the right)allo usersto followspecific itemsand observe patterns between two configurationsusingdifferent a size a stabilized layoutisused taovoid jumps
rified ws ofchange ttribute A. .
4
c
a
Figure 5Animation : ovisualizations f usinglinear to slice and dice,b)interpolation between treem a
interpolation: a)interpolation between two differ ap and scatter a plot, c)interpolation correspond the Yaxison scatter a plot.
Thesimplesttechniqueforanimationislinearinte rpolation. However, when several visual attributes change, lin ear interpolationisconfusing[24]andonlyallowuser stotrack positionchangesabt est. To helpusersunderstandingchanges, wefoundthatanimatingintwostageswasbeneficia l:1)changing positionsand2changing ) dimensions. For scatter-plot-like visualization, position and s independentsothemiddleconfigurationiseasyto Linearinterpolationofpositionsisusedtoreach
ize are compute. themiddle
enttreemap layouts squarified ingtchange ao of attribute for
configurationfromtheinitialandlinearinterpola usedtoreachthe finalconfiguration. Bothshow t exist. Space-fillingvisualizationscomputepositionsacco sizeoitems. f Mostofthem [2,8,11]canbaenim changingthesizeattributessincetheyusealayou stable.Onlysometreemapsdescribedin[3]usela algorithmsthatare notgeometrically stable. Howe generalwaytostabilizealltheselayoutsforstag
tionofsizesis rendswhenthey rdingtothe atedblyinearly thatremains yout ver,we founda e2(i.e.size
5
changes)bycomputingthefinallayoutandlinearly changingthe sizesoall fareasaccording totheir initialvalue This s. isshownin figure4wherethefinallayoutiscomputedbythe “squarified treemap”algorithm,usedbypreviousframeswithth esizes interpolated. Thefinallayouttriestohaveitems as“square”as possible butnotthe previousones. Still,trends are visible. Forstage1(i.e.positionchanges),linearinterpo theonlyoption. Layoutchangesmaybeduetothe algorithmortothechangeofanaxisonascatter sortingorderofda imensionlinkedtoanaxis. Ho changingpositionsandnotsizes,itemsareeasier trendsarenoticeable. Forexample,whenchanging tobemappedontheXaxisoafscatterplot,items along the Yaxis.
lationisusually underlying plot,orthe wever,byonly totrackand theattribute onlymove
cardandmakingdynamicqueriespossiblewithonem on PC. a
illionitems
4.2.4Labeling Textlabelsarenotpreattentivebutareneverthele understandthecontextinwhichvisualizeddataapp eachitemcannotbedonestaticallyodense na visu usedtheExcentricLabeling[9]dynamictechniquef plotandextendeditsdesignfortheTreemap,assh 6and7.
ssimportantto ear. Labeling alizationsowe orthescatter owninfigures
Withtreemaps,texturemappingisveryeffectiveat speeding-up bothstage1and Most 2. space-fillingtechniquesusehierarchical containment,preservedbyattributechangeso nes etofitemsis alwaysinside rectangle a thatcanbw e arpedandtu rnedduring the animationandthereinsoneedtointerpolateeach item separately. Withthistechnique,smoothinterpolationcanbeac hievedwith one millionitem treemaps. Logging/playback:Interactivelyspecifyingmappingsfromdata attributestovisualattributescanbecometedious anddivertsthe userfromthevisualizationanalysis. Toavoidthi sdistraction fromoccurringtoooften,wedevelopedatoolallow ingusersto recordsetsoconfigurations f andviewtheminturn usingtheleft andrightarrowkeys,withanimatedtransitionsas described above. Theseviewsandconfigurations canalsobseavedtofile a andappliedto therdatasets(e.g.tosimplify ro utine examination of similar datasets.)
Figure 7Dynamic : Labeling ofT a reemap (detail). When clickingon the treemap,labelsare displayed dynam icallyon the outside othe f region,which igrayed s out. Th pe opup menu allowsusersto adjustthe depth othe f region ofinterest.
4.2.3DynamicQueries Dynamicqueriesimpliesinteractively filtering and redisplaying of thedatasetthroughcontinuousinteraction. Curren t systemsuse “range-sliders”tofilteroneattributeatatime, eitherchangingthe loweroruppervalue,orsweepingagivenrangeof values betweenthesmallestandthelargest. Thedataset needstobe loadedintomainmemory.Toachievetheredisplay speed requiredforsmoothinteraction,wehavedesigneda technique thatreliesonhardwareacceleration. Whentheuse ar ctivatesa slidertoperformtheseriesoqueries, f alltheit emsaresenttothe graphicsprocessor(GPU)andstoredinadisplayli st. TheZ coordinateicsalculatedaccording tothe attribute being filteredby theslider(e.g.iftheuseristofiltering film a databaseonthesize ofthefilm,thesizeisassignedtotheZ-axis.) Eachtimethe slidermoves,anewnearorfarplanevalueicsomp utedandsent totheGPUandthelistisredisplayed,leavingthe visibility computationtothe hardware. ThistechniqueworksverywellaslongastheGPUh asenough memorytoholdtheitemsandtheshapeoftheitems doesn’t changeduringthedynamicqueries. Usingquadrilat eralsrequires 64MBoffreememory(4verticesperitem,4datape vertex r with 4bytesperdata)foronemillionitemandonlyfit sonspecial machinessuchasSGIO2. Whenapplyingdynamicque rieson scatterplotswith PCgraphicscards,weusepointsinsteadof quads.Currently,OpenGLdoesn’tallowsendingone arrayof pointswithvaryingsizes,althoughitcanbedone withNVidia Vertexprogramsextension. Wesendarraysopf oint sortedby decreasingpoint-size,dividingby4theamountof datasenttothe
Figure 6Dynamic : Labelingon Scatter a Plot(detai ofthe itemsunder the cursor surround the cursor, updated dynamicallyfor gainingrapid a overviewof labelsin an area.
l).Labels and are the item
5.Performance OursystemreadsdataencodedinXMLor dairectory inputformats. Itismadeof23,000linesoC f ++, performancetechniquessuchastemplatemetaprogram toachievetherequiredspeed. Wehaveuseditwit GeForce3boardo2Ghz na Pentium and 3Dlab a Wildca adual1.7GHzPentium.Toscaletoamillionitem, computationoflayoutsshouldbedoneintimelinea numberofitems. Thisitshecasewithsometreema plotsbutnotwithVisDBforexample. Evenusingt techniques,layoutcomputationtakesabout50%oft time. Despitethehightheoreticalperformanceotfheboa notbeenabletogobeyond6millionquadsperseco theboardswteried. Thetheoreticalspeedo15 fm persecondisonlyachievablefortrianglestrips, useforscatterplotsandwouldrequireexpensivec treemaps.
structureas usinghighming[22] hanNVidia 5110 t on the rwiththe psandscatter hefastest heredisplay rds,wehave ndonanyof illiontriangles whichisonf o omputationfor
Combining software and hardware techniques provides a sustainedperformancearound2.5millionquadsper second. By
6
usingtexturemappingforanimatingtreemaps,weac framesper secondfor animating acrossany family o
hieve10 treemap. f
Forscatterplotswehaveonlyreached3framesper animationson1millionitems,and6framespersec dynamicqueries. Findingtechniquesforimproving wouldbeusefulbutthenextgenerationofgraphics computerswillsolve the problem.
secondfor ondfor thatspeed cardsand
Ourestimateitshattheseresultscorrespondtoa improvementonthe available systems.
20to100time
7.Acknowledgments ThisworkwassupportedinpartbyChevronTexaco. thankBenBedersonandBenShneidermanfortheirhe feedback onthe paper. Formoreinformationanddemonstrationofanimation http://www.cs.umd.edu/hcil/millionvis/
6.ConclusionandFuture Work
8.References
Usingasetofnoveltechniqueswewereabletovis ualizeand exploreforthefirsttime1millionitemsona160 0x1200display withoutaggregation. Ourtechniquesrelyheavilyo ncommonly availableacceleratedhardwarefordisplayingitems indense a yet manageablemanner. Wedesignednewanimationtechn iquesto helpunderstandviewchangesandshowtrendsandou tlierswhen theyexist. Wedevelopedamethodtoperformdynam icqueries usingtheZ-bufferofagraphicscardandachieved thespeed required to interact with a million items. Finally , we experimented with non-standard visual attributes su ch as transparencyandstereovisionandfoundthemeffect ivefor temporary inspections.
[2] Baker,M.J.Eick,S.G.SpaceFillingSoftware
Thisworkshowsthatthetechnicallimitsofinform ation 4 visualizationarewellbeyondthetypical10 itemshandledby mostexistingsystems,andopensthedoortonewpo ssibilitiesfor users:alibrarymanagercanuseatreemaptorevie wusage patternsoamillion f individualbooksorganizedwi ththeDewey decimalsystem;largedatabasesohf ighwayincident osjruvenile justicecasescanbefirstexaminedwithoutsamplin gor aggregation. Ourearlyusertestinghasbeenlimitedtocollecti ngfeedback fromcolleaguesaboutthelargetreemapdisplaying ourshared drivesofonemillionfiles.Itseemstoconfirmth atusers’ experienceanalyzingdatatransferstolargevisual izations, allowingthemtomakelocalandglobalcommentson thedata presented. Usersappreciatedthefinepatterns,e. g.thedistinctive patternofwebpagedirectoriesthatcombinetexta ndgraphics, andseemedtoactivelyengagetheirvisualskillst ocompareand makesenseopatterns. f Wehavenowidentifiedtwo applications forusertesting:1)thevisualizationothe f Unive rsity of Maryland catalogandyearsocirculation f data,and2fine ) grain analysisof CensusBureaudata.Theusertestingwillinvolved omainexpert users,whoaremoreapttomakesenseosuch f large datasetsand make suggestionsfor improvements. Thetechniquesweproposedcanbescaledtothelar thatarebecomingavailable,butiw t illrequireth non-standardhardware andsoftware.
gerdisplays ehandlingof
More experimentsare neededtounderstandhow human withalargenumberofitemsandthepossiblelimit visualization,e.g.howcanwebestusevisualizati understandingoftheorganizationandcontentofth itemsof the Library oCongress f AmericanMemory co
can s cope sof ontogainan e7million llection?
Weareconfidentthathumanvisualskillsandthe hardwarewillpushinformationvisualizationmuchf the millionoitems. f
evolutionof urtherthan
Wewantto lpful
ssee:
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