SQL Server 2005 Data Mining

10 Pages·2005·0.233 MB·English

by  Iyer R., Lind J.

SQL Server 2005 Data Mining Page 1
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Informant ByRamanIyerandJesperLind
ContactUs
AdvertisewithUs Theconceptofcross-sellis
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itemsyoumightalsolike. Data

Youcanaddthisfunctionalityto •TotalRecall
yourASP.NETpageby
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employingthepowerofdata
mining,usingsimpleSQL-like Ratethisarticleona
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SQLServer2005,currently nmlkj 4
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BeforedevelopingaWeb
cross-sellapplication,weneed
tobuildtheserver-side
intelligencethatwillenablethe
Tellafriend
applicationtocomeupwith
aboutthisarticle!
smartproduct
recommendations.This
processinvolves:
Preparingthedatayoualreadyhaveaboutpastcustomers
formining;
Designingaminingmodelforthepurposeofmaking
recommendationstonewcustomers;
DeployingthemodeltoAnalysisServerandtrainingitwith
thedatapreparedearlier;and
SettingupsecuritytoallowASP.NETtoquerythetrained
model.

http://www.aspnetpro.com/newsletterarticle/2004/10/asp200410ri_l/asp200410ri_l.asp 09/03/2005 17:34:27
SQL Server 2005 Data Mining Page 2
Thefirstpartofthisarticlewillexplainkeyconceptsand
walkyouthroughtheaboveprocessusingtheSQL
Server2005DataMiningtools.Inthelatterhalfwe’ll
demonstratetheeasewithwhichyoucanintegratethe
predictivepoweroftheminingmodelintoyourASP.NET
Webapplicationusingstraightforwarddatabaseaccess
code.

BeforeYouStart
InstallMicrosoftSQLServer2005AnalysisServicesBeta
2.Thiswillsetuptheservercomponentsaswellasthe
designandmanagementtoolsthatwe’lluseinthisarticle:
BusinessIntelligenceStudioandSQLServer
ManagementStudio,respectively.Wealsorecommend
yougooverthedataminingtutorialincludedwithBeta2.
Note:We’llrefertoyourrunninginstanceofSQLServer
2005AnalysisServicesas“AnalysisServer”intherestof
thearticle.Theserviceshowsupas“AnalysisServices
(MSSQLSERVER)”underServicesinAdministrative
Tools.

MiningYourCustomerPurchaseData
Hereweoutlinetheprocessofbuildingtheback-end
frameworkthatminesyourhistoricalcustomermovie
purchasetransactionsandextractstheknowledgeneeded
tomakecross-sellrecommendationstonewcustomers.
Thisknowledgeisembeddedintheminingmodelthat
we’lldesign.

Datapreparationisanimportantaspectofanydata
miningprocess.WithSQLServerDataMining,however,
itisalsopossibletominethetransactiondatainyour
relationaldatabasedirectly.Forsimplicity,we’lltakethis
approachandassumethatasingletransactiontable
containsyourcustomerpurchaseinformation,asshown
inFigure1.

Figure1:Moviepurchasedatausedbysampleapplication.

TheAnalysisServicesprojectinSQLServer2005
BusinessIntelligenceDevelopmentStudioprovidesthe
frameworkformodelingdataandbuildingaminingmodel
thatlearnscustomerbuyingpatternsfromexistingdata
gatheredfrompriorpurchases.Wethenusethetrained
modeltogeneraterecommendationsfornewcustomers.

Thefirststepistoidentifytheentitywhosebehaviorwe
areinterestedinanalyzingforthepurposeofourcross-
sellapplication.Acaserepresentsallinformation(also
referredtoas“attributes”)knownaboutthisentity.Inthis
scenario,eachdistinctcustomerintheCustomerMovies
tableandthesetofmoviestheypurchasedformsacase.
SQL2005DataMiningusestheconceptofanested
tabletorepresentavariable-lengthcollectionofattributes
ofthesamekindassociatedwithacase.Foreach
customerthereisasetofrowscontainingthelistof
moviespurchased,whichcanberepresentedasanested
table(asshowninFigure2).

Figure2:Theminingmodel’sviewofthecustomermoviepurchasedata.

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SQL Server 2005 Data Mining Page 3
Thedefinitionofacaseanditsassociatedattributesis
knownasaminingstructureinSQL2005DataMining.

Thenextstepistobuildaminingmodelbyselectinga
miningalgorithmandspecifyinghowthecolumnsinthe
miningstructurewillbeusedbythealgorithmtoprocess
theinputdataandextractusefulknowledgefromit.The
AssociationRulesalgorithmisagoodfitforourscenario.
Itlearnswhichitemsarelikelytobeboughttogetherand
utilizesthatinformationtopredictotheritemsgiventhe
itemsthecustomerhasselected.MarkingtheMovies
nestedtableasbothInputandPredictableenablesthe
modeltomakepredictionsusingthemoviesprovidedas
input.NotethattheminingmodelinSQLServer2005
DataMiningisadatabaseobjectthatholdsboththe
definitionoftheinputtotheknowledgeextractionprocess
andtheoutput,whichconsistsofpatternsorrules
learnedbyapplyingtheselecteddataminingalgorithmto
processtheinputdata.

Aswe’llsee,theMiningModelWizardinSQLServer
2005BusinessIntelligenceDevelopmentStudiobuildsa
miningstructureandamodelusingthealgorithmselected
onthefirstpage.

Next,theminingmodeldefinitionissentaspartofa
deploymentpackagetotheserverwhereitistrained.
Trainingcasesconsistofinformationwehavecollected
frompastpurchases.Thedeploymentpackageis
generatedfromtheAnalysisServicesprojectthatwe
designthemodelin,anditincludesbindingstothedata
sourcethatAnalysisServerusesforobtainingthetraining
cases.

Toimprovethequalityofrecommendationsmadefornew
customersthemodelcanbeperiodicallyre-trainedas
morecustomerdataisaddedtothetransactiondatabase.
Forlargedatasetsthiswilltypicallybecarriedoutduring
off-peakhoursoragainstareplicaofthetransaction
database.SQLServerDataTransformationServices
(DTS)canbeusedtosetupapackagetoperformsuch
periodicupdates.

Finally,wemustsetuppermissionssoourapplication
canquerythetrainedmodel.

BuildingaCross-sellMiningModelinSQLServer
2005
Seetheendofthisarticleforinformationabout
downloadingthecompleteAnalysisServerprojectbuilt
usingthestepsoutlinedhereforproducingthemining
modelwe’llutilizeintheASP.NETcodesamplelaterin
thisarticle:
1) CreateanewAnalysisServicesprojectnamed
MovieRecommendationsinBusinessIntelligence
DevelopmentStudio.
2) AddanewDataSourcepointingto
MovieData.mdb,theAccessdatabaseincludedwiththe
sampleproject(availablefordownload;seeendofarticle
fordetails).
3) AddaDataSourceViewbasedonthe
DataSource.Selecttheonlytable,CustomerMovies,
presentintheDataSource.
4) Right-clickontheMiningModelscollectionand
selectNewMiningModeltolaunchtheMiningModel
Wizard.
5) PickFromexistingrelationaldatabaseordata
warehouseontheSelectDefinitionMethodpageandclick
Next.
6) PickMicrosoftAssociationRulesasthedata
miningtechniquetouseonthenextpage.
7) SelecttheDataSourceViewcreatedinstep3.
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SQL Server 2005 Data Mining Page 4
8) Onthenextpage,thesingleCustomerMovies
tablepresentinourDataSourceViewisshown.Markitas
bothCaseandNested.
9) ClickNexttogototheTrainingDatapagewhere
weneedtospecifythecolumnsweareinterestedin
includingfromeachselectedtable(seeFigure3).Inour
scenario,theCustomerMoviestableservesasthesource
forourcasesaswellasthenestedtabledataassociated
witheachcase(thisiswhyitislistedtwice).Fromthe
firstinstancepickCustomerIdasthekeysinceituniquely
identifieseachcustomer.Fromthesecondinstancepick
MovieasKey,Input,andPredictable.
10) ClickFinishtocompletethewizardandbuildthe
MovieRecommendationsminingstructureandmodel(see
Figure4).

Figure3:Selectingcolumnsweareinterestedinmodelingforcross-sellintheData
MiningWizard.

Figure4:Thecross-sellminingmodelinBusinessIntelligenceDevelopmentStudio.

Theabovestepscreateadefinitionofourcross-sell
miningmodelandassociatedobjectsinthedevelopment
environment.Therearetwostepstodeploythemining
modeltotheAnalysisServerandtrainit:
1) Right-clickontheMovieRecommendationsproject
intheSolutionExplorerandselectProperties.Verifythat
theServerpropertyintheDeploymentsectionof
ConfigurationPropertiespointstotheserverhostingyour
AnalysisServicesinstance.Closethedialogbox.
2) Right-clickagainontheMovieRecommendations
projectintheSolutionExplorerandselectDeploy.This
sendstheclient-sidedefinitionstotheserverandinitiates
trainingoftheminingmodel.

WemustsetupaccesspermissionsinAnalysisServer
forIISusingtheSQLmanagementtoolforourASP.NET
applicationtousethetrainedminingmodel:
1) OpenSQLServerManagementStudio.
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SQL Server 2005 Data Mining Page 5
2) ClickConnectintheObjectExplorer,select
AnalysisServer,andconnecttotheAnalysisServer
hostingyourmodel.
3) LocatetheMovieRecommendationsdatabasein
theDatabasescollection,rightclickonRoles,andselect
NewRole.ThisbringsuptheCreateRoledialogbox.
4) OntheGeneralpageenterInternet_Userasthe
rolename.ChecktheReadDefinitioncheckboxunder
Setthedatabasepermissionsforthisrole.
5) ClickMembershipintheleftpaneandselecta
page.AddyourIISuser(thedefaultis
IUSR_machinename)totherolebyclickingAdd.
6) NowclickMiningStructureintheleftpane.This
showstheCustomerMoviesminingstructureandthe
CustomerMoviesminingmodelownedbythemining
structure.DropdownthepermissionlistunderAccess
andselectReadforbothobjects.AlsochecktheRead
Definitioncheckboxforboth.
7) ClickOKtoaddtheRolewiththeabove
permissionset.

RecommendingProductsBasedontheUser’s
ShoppingBasket
Nowwe’rereadytoproducemovierecommendationsin
ourWebapplicationbyrunningaSQL-likequeryagainst
theAnalysisServerthatholdsourtrainedminingmodel.

We’veputtogetheraminimalapplication(showninFigure
5)thatdemonstratestheideasbehindarealdeployment,
focusingonthegenerationofthepredictionqueryfor
gettingrecommendations.TheWebcustomeris assumed
tohaveoneormoreitemsintheshoppingbasket,andfor
simplicitywehaveatextboxwhereitemscanbeentered
manually(separatedbysemicolons).ClickingAddItems
toCartdisplaystheitemsintheshoppingbasketandalso
showsalistofrecommendations.

Figure5:Asimpleshoppingbasketapplication.

ThecodebehindthebuttonclickisshowninFigure6;
youcanseethatit’squitesimple.

PrivateSubButton1_Click(ByValsenderAs
Object, _
ByVal eAsSystem.EventArgs) 'Handles
Me.Button1.Click

'ParsetheinputintoanArrayListofstrings.
DimalInputItemsAsNewArrayList()
DimsplitcharAsChar() ={";"c}
DimszInputItemsAsString() =
Me.TextBox1.Text.Split(splitchar,20)
DimiAsInteger
Fori = 0ToszInputItems.Length - 1
alInputItems.Add(szInputItems(i).Trim())
Next i

'Additemstotheshoppingbasket.
dgShoppingBasket.DataSource =alInputItems
dgShoppingBasket.DataBind()

'Gettop5recommendations.
DimalRecommendedItemsAsNewArrayList(5)
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SQL Server 2005 Data Mining Page 6
GetRecommendations(alInputItems,
alRecommendedItems)

'Displayrecommendations.
dgRecommendations.DataSource =
alRecommendedItems
dgRecommendations.DataBind()
EndSub'Button1_Click
Figure6:Populateshoppingbasketandrecommendations.

ThemethodbuildsanArrayListofstringsfromtheitems
intheshoppingbasketandpassesittothe
GetRecommendationssubroutine,requestingthetopfive
recommendationsbasedontheinputitems.Weusetwo
DataGridobjects:dgShoppingBasket,toholdtheitemsin
theuser’sshoppingbasket;anddgRecommendations,to
displaythegeneratedrecommendations.Thereal
workhorseistheGetRecommendationssubroutine.

ThecoreoftheGetRecommendationssubroutineisthe
constructionofthepredictionjoinquery(seeFigure7)
thatgetssenttoAnalysisServerandreturnsthelistof
fiverecommendations.

SELECTFLATTENED
TopCount(Predict([CustomerMovies],
INCLUDE_STATISTICS),
$AdjustedProbability,
5)
FROM[MovieRecommendations]
NATURALPREDICTIONJOIN
(SELECT (SELECT'StarWars' AS[Movie]
UNION
SELECT'TheMatrix'AS
[Movie] )
AS[CustomerMovies] )AS t
Figure7:ObtainrecommendationsusingDMXpredictionjoinagainstminingmodel.

TheSQL-likequerylanguagesupportedbyAnalysis
ServerforqueryingminingmodelsiscalledDMX.The
DMXqueryinFigure7performsapredictionjointhat
takesthetwomoviesfromtheuser’sshoppingbasket,
formsacase,andjoinsitwiththeminingmodelto
produceanoutputrowsetcontainingalistofpredicted
recommendations.

TheGetRecommendationssubroutine,showninFigure8,
storesmostofthequeryincompile-timestringtemplates.
Whatneedstobefilledinistheinputdata.

PrivateSharedSubGetRecommendations( _
ByValvInputItemsAsArrayList, _
ByRefvRecommendedItemsAsArrayList)

'TemplatesforgeneratingDMXpredictionjoin
statement.
DimstrDMX1AsString = _
"SELECTFLATTENEDTopCount(" + _
"Predict([CustomerMovies],
INCLUDE_STATISTICS)," + _
"$AdjustedProbability,5)From[Movie
Recommendations] " + _
"NATURALPREDICTIONJOIN(SELECT("
DimstrDMX2AsString =")AS[Customer
Movies])ASt"

'Iterateshoppingbasketandproduceinput
case.
DimcItemsAsInteger=vInputItems.Count
DimstrDMXAsString =""
DimiAsInteger
Fori = 0TocItems - 1
DimitemAsString =vInputItems(i).ToString()
item=item.Replace("'","''")
strDMX+="SELECT " +"'"+item+"'AS"+
"[Movie]"
Ifi <cItems - 1Then
strDMX+= "UNION "
EndIf
Next i

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SQL Server 2005 Data Mining Page 7
'PuttogetherDMXpredictionqueryto
'get5recommendations.
strDMX =strDMX1 +strDMX +strDMX2

'ConnecttoAnalysisServerandexecutequery.
DimasSessionAsNewAnalysisServerSession
asSession.Connect()
IfFalse =
asSession.ExecuteAndFetchResult(strDMX)Then
Return
EndIf

'Readpredictionresultsandbuildlist
'ofrecommendations.
vRecommendedItems.Clear()
WhileasSession.asDataReader.Read()
DimtypeAsString =
asSession.asDataReader.GetDataTypeName(0)

Iftype="DBTYPE_WVARCHAR"Ortype="String"
Then
Try
DimvalAsString =
asSession.asDataReader.GetString(0)
vRecommendedItems.Add(val)
CatcheAsException
Console.WriteLine(e.Message)
EndTry
EndIf
EndWhile

'DisconnectfromAnalysisServer.
asSession.DisConnect()

EndSub 'GetRecommendations.
Figure8:Convertshoppingbaskettoinputcaseforminingmodelpredictionandget
recommendations.

Asexplainedbrieflyduringtheminingmodeldesign
phase,dataispassedtominingmodelsintheformof
cases.Eachcaserepresentsalltheinformationwehave
aboutasinglecustomer.Whenwemakeapredictionfor
anewcustomer,theknowninformationaboutthe
customerispassedasaninputcasetotheminingmodel.
Inthisscenariotheinputcaseisthelistofmoviesinthe
customer’sshoppingbasket.

Notethatthelistofmovieswasrepresentedasanested
tableintheminingmodeldefinition;therefore,weneedto
supplytheinputcaseinthesameform.Thisis
accomplishedbythenestedsub-selectafterthe
NATURALPREDICTIONJOINclauseinFigure7.The
UNIONoperationisusedtoaddeachmovieasa
separaterowinthenestedtable.Whenwesupplycases
inlinelikethis,wedon’tneedtoprovideavalueforthe
casekey,CustomerId,sincethatisnotusedforthe
predictionoperation.

Withtheaboveinputcasedataformatinmindit’snow
easytowalkthroughtheGetRecommendationscodeand
understandwhatit’sdoing.

Westartthequerygenerationprocessbydeclaring
templatesfortheDMXstatement.Wetheniterateover
theinputitems,thecontentsoftheuser’sshopping
basket,toproduceaninputcasewithanestedtable
formatasdescribedabove.Theinputcaseisappended to
therestofthetemplatetoproducethequerythatwillbe
senttotheserver.

Next,aninstanceoftheAnalysisServerSessionclassis
instantiatedtoconnecttoAnalysisServerandexecutethe
queryagainstthetrainedminingmodel.Thecodeforthis
classisincludedinthedownload.The
AnalysisServerSessionclassusesADOMD.NET,the
managedproviderforAnalysisServer,toconnecttothe
server.ADOMD.NETimplementsthesameconnection,
command,anddatareaderinterfacesasstandard
ADO.NET(System.Data.Oledb),sotherestofthecode
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SQL Server 2005 Data Mining Page 8
looksexactlylikeADO.NETcode—butoffersbetter
performance.(TouseADOMD.NETyou’llneedtoadda
referencetoMicrosoft.AnalysisServices.AdomdClient.dll,
installedbytheSQLServer2005Beta2Client
ComponentsSetup.Ifitisnotlistedunder.NETinthe
AddReferencedialogboxinVisualStudio,browseto
ProgramFiles\Microsoft.NET\ADOMD.NET\90andaddit
fromthatlocation.)

Thequeryresultsarefetched,againusingastandard
datareaderinterfaceexposedbyADOMD.NETandthe
arraylistofoutputitemspopulatedwiththe
recommendationsreturnedbythepredictionjoin.Note
thattheDMXquerywegenerateusestheFLATTENED
keywordtoavoidhavingtoperformhierarchicalresult
navigationtofetchtheresults.

UndertheHood
Inthissectionweexplainwhathappensontheserver
whenitreceivesapredictionqueryandhowthe
knowledgeacquiredbytheminingmodelmaybeexplored
further.

Duringthetrainingprocess,theAssociationRulesmodel
learnsasetofrulesthatareusedtogenerateproduct
recommendations.Ifarulesuchas“[Camera,Film]->
Batteries”wasdiscoveredandthecustomer’sshopping
basketcontainsCameraandFilm,thenthisrulefires.Of
course,theremaybeotherrulesthatpredictBatteriesas
well,inwhichcasetherulewiththehighestscoreis
used.Thescoreassignedtoarule,alsoknownasits
Importance,takesintoaccountandcompensatesforthe
factthattheprobabilityforarulemaybehighjust
becausethetargetitemispopularinthedataset.Assume
that10%ofcustomersbuy StarWars irrespectiveof
otherpurchases,and BladeRunner isboughtbyjust3%.
Iftworulespredict StarWars andBladeRunner withthe
sameprobability,thescorefor StarWars willbelower
sinceit’ssopopular.However,theadvancedusercan
finetunethescoreusingalgorithmparameters.

Finally,thetop nhighestscoringrulesareusedto
generatetherecommendations.Thelastparameterinthe
TopCountfunction(again,seeFigure7)setsanupper
limitonthenumberofitemsreturnedbytheprediction
algorithm.

Therulesareorganizedbytheitemspredictedandsorted
indescendingorderbasedonthescore.Theprediction
algorithmcanavoidlookingatverylargesetsofrulesto
achievegoodpredictionperformance.

Additionalinsightintoacustomer’spurchasingbehavior
maybegainedbyusingtheviewerssuppliedaspartof
theBusinessIntelligenceDevelopmentStudio.These
includetheAssociationRulesViewer(showninFigure9)
forbrowsingtherulesandtheDependencyNetViewer
thatgraphicallyshowstherelationshipbetweenitems.
Thegraphlayoutalgorithmillustrateshowthestrongest
correlatedproductsareclustered.

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SQL Server 2005 Data Mining Page 9
Figure9:Viewingmodelcontent.

TheAssociationRulesviewerdisplaystheimportancefor
eachruleandrankstherulesbythisscore.Asexplained
earlier,importanceservesasabettermeasurethan
probabilityforfindinginterestingrulesinyourdata.

Conclusion
Thisarticlemerelyscratchesthesurfaceofthe
possibilitiesthatSQLServer2005DataMiningpresents
foryourASP.NETapplications.Oneideaforextending
thissamplewouldbetoaddothercustomerattributes
suchasdemographicsasinputstopotentiallyimprovethe
qualityoftherecommendations.OtherpossibleWeb
applicationsinclude:
Targetedadsbasedonanalysisofbrowsingbehaviorusing
theSequence_Clusteringalgorithm.
AnonlineHelpdeskthatfindsthemostappropriateanswer
forauser’squerybyusingtheNeural_Networkalgorithmin
conjunctionwithtextminingtechnologiesavailableinSQL
Server2005DataTransformationServices.

ThedataminingtutorialforSQLServer2005Analysis
ServicesisavailableontheReadinessKitCDincluded
withtheBeta2package.VisittheMicrosoftbeta
newsgroupat
microsoft.beta.yukon.analysisservices.dataminingorthe
DataMiningforumat http://www.sqljunkies.com/Forums/
ShowForum.aspx?ForumID=38ifyouhavequestionsabout
SQLServer2005DataMining.Thisarticleoffersan
outlineofthesimplifiedminingprocessusedforthis
specificscenario.Forageneralizedversionofthis
methodology,refertoCRISP-DMat http://www.crisp-
dm.org.

Thesamplecodeinthisarticleisavailablefor download.

RamanIyer isaSoftwareDesignEngineeratMicrosoft
Corp.andafoundingmemberoftheSQLServerData
Miningdevelopmentteamthere.Hecanbereachedat
mailto:[email protected].

JesperLind isaResearchSoftwareDesignEngineerat
MicrosoftResearchandamemberoftheMachine
LearningandStatisticsteam.Hecanbereachedat
mailto:[email protected].

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SQL Server 2005 Data Mining Page 10
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