Lecture 25 EDA - GitHub Pages · Lecture 25: EDA Theodoros Rekatsinas 1. Data Visualizations Today...

CS639:DataManagementfor

DataScienceLecture25:EDA

TheodorosRekatsinas

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DataVisualizationsToday

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DataVisualizationsToday

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StandardDataVisualizationRecipe:

1. Load datasetintodataviz tool2. Start withadesiredhypothesis/pattern(explore

combinationofattributes)3. Select viz tobegenerated4. See ifitmatchesdesiredpattern5. Repeat 3-4untilyoufindamatch

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TediousandTime-consuming!

KeyIssue:

Visualizationcanbegeneratedby:varyingsubsetsofdatavaryingattributesbeingvisualized

Toomanyvisualizationtolookattofinddesiredvisualpatterns!

1.Visualizationrecommendations

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Whatyouwilllearnaboutinthissection

1. SpaceofVisualizations

2. RecommendationMetrics

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Goal

Givenadatasetandatask,automaticallyproduceasetofvisualizationsthatarethemost“interesting”giventhetask

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Particularlyvague

Goal

Givenadatasetandatask,automaticallyproduceasetofvisualizationsthatarethemost“interesting”giventhetask

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Example

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• Dataanalyststudyingcensusdata• age,education,marital-status,sex,race,income,hours-workedetc.

• A =#attributesintable

• Task:Compareonvarioussocioeconomicindicators,unmarriedadultsvs.alladults

Spaceofvisualizations

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Forsimplicity,assumeasingletable(starschema)

Visualizations=agg.+grp.byqueries

Vi=SELECTd,f(m)FROMtableWHERE___GROUPBYd

(d,m,f):dimension,measure,aggregate

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Spaceofvisualizations

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Vi=SELECTd,f(m)FROMtableWHERE___GROUPBYd

(d,m,f):dimension,measure,aggregate{d} :race,work-type,sexetc.{m} :capital-gain,capital-loss,hours-per-week{f} :COUNT,SUM,AVG

Goal

Givenadatasetandatask,automaticallyproduceasetofvisualizationsthatarethemost“interesting”giventhetask

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Interestingvisualizations

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Deviation-basedUtility

Avisualizationisinterestingifitdisplaysalargedeviationfromsomereference

Task:compareunmarriedadultswithalladults

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V1

V2

V1

V2

Compareinduced

probabilitydistributions!

Target Reference

V1=SELECTd,f(m)FROMtableWHEREtargetGROUPBYdV2=SELECTd,f(m)FROMtableWHEREreferenceGROUPBYd

Deviation-basedUtilityMetric

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Avisualizationisinterestingifitdisplaysalargedeviationfromsomereference

Manymetricsforcomputingdistancebetweendistributions

V1

V2

D[P(V1),P(V2)]

Earthmover’sdistanceL1,L2distanceK-Ldivergence

Anydistancemetricb/ndistributionsisOK!

ComputingExpectedTrend

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Racevs.AVG(capital-gain)ReferenceTrendSELECTrace,AVG(capital-gain)FROMcensusGROUPBYrace

P(V1)

Expected

Distribution

ComputingActualTrend

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Racevs.AVG(capital-gain)TargetTrendSELECTrace,AVG(capital-gain)FROMcensusGROUPBYraceWHERE marital-status=‘unmarried’

P(V2)

Actual

Distribution

ComputingUtility

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U=D[P(V1) ,P(V2)]D =EMD,L2etc.

LowUtilityVisualization

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Actual

Expected

HighUtilityVisualization

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Actual

Expected

Othermetrics

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• Datacharacteristics• TaskorInsight• SemanticsandDomainKnowledge• VisualEaseofUnderstanding• UserPreference

2.DB-inspiredOptimizations

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Whatyouwilllearnaboutinthissection

1. RankingVisualizations

2. Optimizations

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Ranking

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Acrossall(d,m,f),whereV1=SELECTd,f(m)FROMtableWHEREtargetGROUPBYd

V2=SELECTd,f(m)FROMtableWHEREreferenceGROUPBYd

Vi=(d:dimension,m:measure,f:aggregate)

10sofdimensions,10sofmeasures,handfulofaggregates

2*d*m*f

è100sofqueriesforasingleusertask!

èCanbeevenlarger.How?

Goal:returnkbestutilityvisualizations(d,m,f),(thosewithlargestD[V1,V2])

Evenlargerspaceofqueries

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• Binning• 3dimensionalor4dimensionalvisualizations• Scatterplotormapvisualizations• …

Backtoranking

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Acrossall(d,m,f),whereV1=SELECTd,f(m)FROMtableWHEREtargetGROUPBYd

V2=SELECTd,f(m)FROMtableWHEREreferenceGROUPBYd

Goal:returnkbestutilityvisualizations(d,m,f),(thosewithlargestD[V1,V2])

NaïveApproachForeach(d,m,f)insequence

evaluatequeriesforV1(target),V2(reference)computeD[V1,V2]

Returnthek(d,m,f)withlargestDvalues

IssueswithNaïveApproach

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•Repeatedprocessingofsamedatainsequenceacrossqueries•Computationwastedonlow-utilityvisualizations

Sharing

Pruning

Optimizations

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• Eachvisualization=2SQLqueries

• Latency>100s• Minimizenumberofqueriesandscans

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50 100 250views

late

ncy

(s)

dbmsCOLROW

Optimizations

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• Combineaggregatequeriesontargetandref

• Combinemultipleaggregates(d1,m1,f1),(d1,m2,f1)à (d1,[m1,m2],f1)

• Combinemultiplegroup-bys*(d1,m1,f1),(d2,m1,f1)à ([d1,d2],m1,f1)Couldbeproblematic…

• ParallelQueryExecution

CombiningMultipleGroup-by’s

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• Toofewgroup-bys leadstomanytablescans

• Toomanygroup-bys hurtperformance• #groups=Π (#distinctvaluesperattributes)

• Optimalgroup-bycombination≈bin-packing• Binvolume=logS(maxnumberofgroups)• Volumeofitems(attributes)=log(|ai|)• Minimize#binss.t.

Σi log(|ai|)<=logS

Pruningoptimizations

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• Keeprunningestimatesofutility• Prunevisualizationsbasedonestimates

• Twoflavors• VanillaConfidenceIntervalbasedPruning• Multi-armedBanditPruning

Discardlow-utilityviewsearlytoavoidwastedcomputation

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Visualizations

Queries(100s)

Sharing

Pruning

Optimizer

DBMS

MiddlewareLayer

Moreonautomatedvisualizations

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ZQL:aviz explorationlanguage

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Intelligentqueryoptimizer

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Summary

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Humanintheloopanalytics

areheretostay!