Four new ANSI SQL relational processing discoveries and their 15 breakthrough capabilities, fundamental principles, and operation are explained in this presentation producing a new generation of powerful semantic SQL.

1. Slide1:New Relational DiscoveriesProduce NewGenerationinSQLSemanticCapabilities
Four new ANSISQLrelational processingdiscoveriesandtheir 15 breakthroughcapabilities,
fundamental principles,andoperationare explained inthispresentation. These capabilities add
powerful new operations whileeliminatingproblemSQLareas.The firstdiscoverysupportsadvanced
capabilitiesthroughthe natural integrationof multipathhierarchical processing intothe frontendof the
relational processing.Thissupportsrelational processingin apowerful hierarchical multipathnonlinear
fashion. ThisisbothrelationallysoundandhierarchicallyprincipledusingstandardSQLsyntax. Ontop of
thisSQL hierarchical processingmodel,apowerfulnetwork structure iscontrolled bydynamically
referencingdataitems acrosspaths. Thisusesa second powerful processingdiscovery of inherentLCA
processingthatnaturally determines andprocesses the semanticmeaningacrosspathways
automatically.These capabilities produce anextremely powerfulandadvanced new generation of SQL
semanticoperation.

2. Slide2:Powerful NewSQL Hierarchical CapabilitiesCanbeUtilizedTogether
The 15 new breakthroughsinhierarchical ANSISQLonthisslide are made possible by the new relational
processingdiscoveries.Theyare extremelypowerful and canbe usedinany combination synergistically
increasingtheircapabilities.Thiscreatesnew usesandcapabilitiesnotpreviouslyavailable orpossible.
These will covereachof these newcapabilitieslistedonthisslide usingvisual examples.The current
bulletedsubtopicbeingshownanddescribedineachfollowingslide will be highlighted andunderlined.
A coloredarrow may alsobe usedto draw attentionto active areasinthe current slide.The following
slidesare connectedwiththe mainheadingorbulleteditembeingdescribed. Someinfomaybe
repeatedacrossslidestoestablishcontext.

3. Slide3:Defining theSQL MultipathHierarchical DataModel
StartingwithSQL’snewhierarchical datamodeling of relational tablesonthisslide,itisshown how
relational tables (ornodes) A,B& C can be dynamically modeledhierarchicallyusingonly the SQLLeft
Joinoperation. Itisusedto establishthe dynamichierarchical datamodelledroadmapusedto control
the active query.The introductionof LEFT Joins inthe SQL-92 standard enablespowerfulhierarchical
structuresto be modeled.Non-hierarchical datamodelingwilltriggeranerrorconditionpreventing
incorrecthierarchical operation.The inherentlysupportedSQLmultipathhierarchicallymodelled
structure naturallyenables powerful concurrentmultipathhierarchical LCA processingforderivinga
solution frommultiple paths.Thisenables SQLtoconcurrently testdatafrommultiple pathwaysto
naturally produce apowerful semanticallymeaningful LCA result.The WHEREclause can alsobe usedto
reference dataitems acrossthe data model pathways inapowerful networkfashion.

4. Slide4: IntegratingHierarchical Processinginto RelationalProcessing
Thisis the 1st
of 4 newrelational breakthroughdiscoveries.SQL’sstandardrelational processing
naturallyutilizes the hierarchical processingcapabilitiesof only the SQL-92Left Outerjoin where less
syntax produces more powerful processing.Itpreservesdataonthe leftside of the LeftJoinoperation
whenthere are no matchingdata valuesonthe rightside.Thismakesitoperate fully hierarchically. The
multiple “ON”clausesreplace the oldersingle WHEREclause allowingittoprecisely datamodel
hierarchical multipathstructures.Thisdatamodelingis shownatthe greenarrow.With the redarrow at
the start of the hierarchicallymodelledstructure,nodesA,B,C,D and E are modelledinturn.They each
add a path usingthe ON clausesto preciselyconnect eachnode asshown. Whenthe multipath
hierarchicallymodelledSQLat the greenarrow is processed, SQLisoperatingata greatly increased
semanticprocessinglevelbecause the semantics are known fromthe datamodelingalreadyapplied.

5. Slide5:Hierarchical Processingis Usedin Relational Processing
The inherenthierarchical processingpossible instandardSQLisan importantanduseful basicdiscovery.
It has shownthatpowerful hierarchical processingisasubsetof relational processingandcanbe used
for complete natural integration.Thisalsoallowsrelationaldataindependence andhierarchical data
modelingtonaturallycombinethe advantagesof both.The redarrow pointsto the box that usesonly
Leftouterjoinsoperationstoenable powerfulhierarchical multipathdatamodelingandprocessing. The
hierarchical processingfollowsnatural datapreservationprinciples.Thisaddstothe inherent
correctnessalongwithrelational processing’spowerful mathematical foundation.Thisproducesanew
semanticSQLoperatingat a much higher processinglevel.Itinherentlyunderstandshierarchical data
modelingandprocessingthatnaturallyutilize new SQLhierarchical semantics. Thisalsomeansthat
there is no newcode to support.

6. Slide6:SQL Hierarchical ProcessingSupportsOnly Structured Data
UsingSQL to supportfull multipathhierarchical processingrequireslimitingthe processingtostructured
data. ThismakesSQL more powerful andeasiertouse usingonlypowerful structuredprocessing.This
meansthere are onlysingle pathstoeach node type inthe structure diagramstartingfrom the red
arrow. Thismakesthe hierarchical structure unambiguousenablingittobe naturally navigatedeven
withitsnew more powerful hierarchical structure capability. This natural internal navigationoperates
by nothavingto make any navigational pathselectiondecisions. All referencednodesare accessedusing
onlya single path.Thisunambiguous automaticnavigationof hierarchical structuresintegrates naturally
withrelational processing’sstandard navigationlessaccess.

7. Slide7:SQL Hierarchical Data ModelingLanguage has Principles
SQL’s seamlesshierarchical datamodelinglanguage andsyntax shownatthe redarrow isbasedon well-
knownhierarchical datapreservationprinciples.A parentnode canexistW/Oa child,buta child cannot
existW/Oa parentand a childcan have onlyone parent. If thisis followed,it resultsinnatural
correctness.ThismeansSQLhierarchical processingisbasedoncombinedrelational andhierarchical
principles.Itstill supportsboththe dataindependence of relational andthe semanticsinhierarchical
structures.Thisallowsanyhierarchical andflatstructurestobe dynamically modeled togetherinany
way and processedas semanticallyrich hierarchicalstructures.Relational processingutilizing dynamic
hierarchical processing nowbecomesextremelypowerful anduseful for accurate complex processing.

8. Slide8UsesON Clausesand Not WHERE Clause for Data Modeling
Startingat the redarrow,it can be seenhow multiple ON clausesare muchmore precise fordata
modelingthanthe oldersingleWHEREclause was.Anotherreasonforthisisthat the ON clause
operatesmore locally. Itonlyaffectsthe paththatit isused on and onlyfromitsinitial pointof use
downward.Thisalsoincreasesthe precisenessof the datamodeling.The WHEREclause isused now
only forglobal operations whichcanselectivelyaffectanynodesinthe entire structure. Thisisavery
powerful operationinitsownright. Soit shouldbe usedonlyforglobal hierarchical datafilteringand
leave the ON clause formore local and precise datamodelingoperationswhere itismore useful and
flexible. Thisseparationof dutiesmakeseachoperation more powerful anddistinct.

9. Slide9:Use the WHERE Clause for Hierarchical Global Data Filtering
Unlike the local ON clause,the WHERE clause isglobal and can specify dataanywhere in the structure to
be matched.Thisis shownbypreservingdatathatreliesonWHERE C.val=‘C2’ bythe redarrow. When
the “C2” value matches,the searchgoesinall directions from‘C2’to the SELECTed data typesinnodes
A,B, C, D andE to retrieve them.NodesDandE are below node C, root A isabove.If a node A data
occurrence match isfound,the pathwill deflect naturallyand hierarchically filtereddowntonode B
because itsparentexists whichisstandardSQLoperation. Thisisshowninboththe flat relational
structure and itshierarchical ViewX datastructure where the shadedboxesrepresentthe data
retrieved. Atthe blue arrow,itisshownthatthe relational structure canbe automatically convertedto
itshierarchical internal representation makingiteasiertoutilize. Thisisachievedbyremovingreplicated
data, while preservingduplicate data byusinga new duplicate datadata-type.

10. Slide10: MultipathConcurrentHierarchicalProcessing
Hierarchical structuresare composedof parent nodesandtheirchildreninahierarchical fashion.With
thisbasichierarchical processing,parentsare singularwithonlyone pathinandany numberof paths
out supportingmultiplepathways.Thismakesthe multipathstructures alsounambiguous,allowingitto
be accessedschema-free inanavigationlessfashionasisstandardfor SQL. The redarrows inthe
diagramshow examplesof multiplehierarchical pathwaysthatwill naturallysupportpowerful multipath
concurrenthierarchical processing.Thisadvanced multipathprocessingenables producingasingle
query resultusingthe natural LCA processingshown.

11. Slide11: Multiple Data Occurrence OrganizationFullySupported
Multipathhierarchical structures cansupportthe powerful feature of multiplenode occurrences.These
are showninthe currentslide where nodesB, C,D and E each have multiple occurrences. Notice that
node occurrencesE1 and E2 are locatedunderoccurrence C1 while node occurrencesE3and E4 are
undernode occurrence C2. Theyare inseparate node occurrence groupsandcannot be processed
togetherbecause they have separate parentoccurrencesC1andC2. This supports a muchhigherlevel
of data organization thatnaturally processes the dataoccurrences. Thisenables multiplenode
occurrences to have theirownsetof data combinations makingtheiroverall operationmore flexibleand
precise.

12. Slide12 MultipathConcurrent ProcessingGreatlyIncreasesAnalytics
The two differentqueriesatthe big greenarrow produce the same internal hierarchical processing
loopsbecause theyuse the same structure shownandthe same SELECTed multipathlocationsatnodes
B, D and E. This producesresultstailoredtotheirdifferentqueryspecifications shown.The multipath
hierarchical processingrequires very specialprocessingforqueries connectingdataandgenerating
semantics acrosspathways. ThisisknowntechnicallyandacademicallyasLowestCommonAncestor
(LCA) processingwithitsnew use now naturally performedby SQLmultipathhierarchical processing.
Thisconcurrentmultipathhierarchical processing showninred can be furtherenhanced byreferencing
across active pathwayswhich naturallyutilizes LCA processing.Thisgreatlyincreases the analytical
processingcapabilitiesinnew,more:meaningful,accurate andpowerfulways byutilizingconcurrent
multiple active andconnectedpathwaysthatcansupportpowerful networkstructures.

13. Slide13: InherentLowestCommon Ancestor (LCA) MultipathProcessing
The 2nd of 4 relational breakthroughdiscoveriesisthe verypowerfulLCA processing enginefound
operatingnaturallyandinherentlyinANSISQL.Olderphysical hierarchical structuresrequiredacomplex
searchfor LCAs.For example,the datareferencesB,D& E showninred arrowswouldhave required
searchingupwardsfromthe referencednodesB,D andE to locate LCA nodesC and thenA.But SQL
hierarchical LCA processingisoccurringinherentlyinSQLrequiringnosearchingorcoding.Thisnatural
LCA processingutilizesthe relational processing’sCartesianproduct’soperation. The generated
Cartesianproductcontrollingthe searchupto the LCAs is shown inred.LCAs are at the connectionpoint
where the pathways meet. Thisnatural operationenablesLCA’soperationtoany nestinglevel. This
natural LCA processingis necessary because its required ability canbecome toocomplex orcostly to
code by hand.

14. Slide14: LCA Naturally Determinesthe Most Meaningful Results
LCA processingis naturally triggeredby aWHERE clause reference to connectmultiple pathways shown
inred. Bothof these queriesare shownonthisslide atthe biggreenarrow.Thisproducesa combination
of valuesusedtotestfora matching datacombinationproducedfromthe inherentCartesianproduct
shownbythe blackbox.Thisresultsinthe tightestmostlimitingrange of datareferencesforthe active
queryto derive the mostmeaningful resultusingthe smallestprocessingarearequired.Thisisnaturally
correct and takesintoaccount all the differentmultipathqueryreferences. Any numberof nodescanbe
connectedacrosspathwaysgreatlyincreasingthe analytical queryingpower. Thismakesconcurrent
multipathprocessingwithLCA processing very accurate andefficient.The Cartesianproductdata
aroundan LCA extendstoitslowestnode references, DandE fornode C and thenB and C for node A as
shownonthis slide.

15. Slide15: LCA ProcessingCan AlsoInclude Multiple LCA Nesting
Thisdiagramshows howLCA processinginthe redcircle occurs and nestsnaturallywhen multiple LCAs
are needed.Asfew LCAsasnecessarywill be naturally usedacrossmultiple pathways showningreen.
Two pathreferencestriggersanLCA processing,athirdtriggersanotherone andso on fromthe bottom
up.This keepseachLCA processingassmall as possible withnatural LCA nestingshownbythe red
upwardarrows. This iscontrolledbythe relational Cartesianproduct processing.WhenSQLis
performinghierarchically,itsCartesianproductisnaturallyperformingthe requiredLCA processing,so
the operationistransparent occurringnaturally. Iwasnot aware of thisLCA operationoccurring
inherently until Irealizedsomething hadtobe naturallycausingit because the multipathresultswere
alwayscorrect.I found thisnatural LCA processingin the Cartesianproduct controlledby the initial
hierarchical processing model.

16. Slide16: MultipathHierarchical StructuresCan ProcessNetworks
Multipathhierarchical structures use LCA processingtoenable nodesinthe structure tobe connected
by referencingtheirdata.Forexample,nodereferencesBandY bythe greenarrows are notdirectly
connected,butcan be naturally connected atLCA node A by referencingasin“SELECT B.bWHERE
Y.y=4” inred. AddingNode Zat the orange arrow, “SELECT B.b WHERE Y.y=Z.z”in redconnectsall three
B, Y, Z nodes.ThisnestsLCA X underLCA A. The bottomleftshowsthe white structure asthe underlying
hierarchical model boxing-inLCA operation.All 25connections possiblefromthe 7 nodes are shown in
blackat bottomleft.These canbe createdbya single WHEREclause reference usingANDandOR
operationslinkingthemtogether. Thisenablesall connectionstobe testedtogetherbecause every
node can directlyreference everyothernode asshownbythe blacklinesoverthe white lines.
Thissupportsan extraordinarilypowerfulnetworkeddataanalysis frommanyconcurrentdirections.
Operational Overview of Semantic SQL with Concurrent Multipath Networking
1 2 3
3
Hierarchical DataModeling WHERE Clause Use Networking
Define desired
hierarchical datamodel
and multipathprocessing
usinginput:flattables,
nodesandstructure
views. Thenmove to
WHERE clause at position
2 to performWHERE
clause.
Dynamicallycompose
and execute WHERE
clause to create complex
networkacrossdata
model nodes from
position1.This allowsall
nodestobe connectedin
any way at position2 as
shown above.
Networkingcompletes
LCA processingatposition
3. Then the usercan go
back to position2to
specifyanotherqueryor
the usercan go back to
position1to re-specifya
new data model andre-
start fromposition2.

17. Slide17: DynamicLogical Hierarchical StructureJoining
The red arrow pointstohierarchical structure ABCbeingcreatedina view.The view isusedby
referencingitsname, ABC.The greenarrow pointstothe structure beingdefinedwhichismodelled
behindthe greenarrow. Hierarchical physical structuresare now backagain withthe introductionof
XML. They are more powerful thanbefore withthe discoveryof SQLinherent multipathhierarchical
processinginrelational databases. Withlogical hierarchical processing,multipathhierarchical structures
and viewscanbe hierarchicallycombineddynamicallyinany orderandthenprocessed.These new
powerful logical structuresare alsoveryefficientbecause theyare temporaryandnaturallyfreed-up
afterthe querycompletes. Thisnew SQLhierarchical processing canbe fullydynamicandlogical.This
adds significantflexibility increasinganalysis.

18. Slide18: UserDoes Not Need to Know the Data Structure to Query
Aftera logical structure is dynamically created,itisprocessedasa single structure.Inaddition,
hierarchical structurescanalsobe heterogeneouslycombinedfrom:fixed;dynamic;remote;andview
structures whichare also processedasa final logical structure. Mostimportantly,the userdoesnot
needtoknowthe structure or have to navigate the heterogeneousmultipath structure.Thisisbecause
all typesof hierarchical structuresare unambiguouswithonlyasingle pathtoeachnode.Thisallows
navigationlessschema-free navigation regardlessof how the final heterogeneous hierarchical structure
iscomposed.

19. Slide19: JoiningStructuresIncreases DataValue&Semantics
The increasingof hierarchical semanticsbydynamicallycombiningstructuresorpartsof structuresalso
resultsineverincreasingdatavalues.Asmultipathstructurescontinue togrow downwardsshownby
the red arrows,theysplitpathscontinuallyincreasingthe numberof paths.Asthis occurs,the data and
impliedsemantics are sharedacrossmore andmore pathsincreasingdatavalue andsemanticswhich
are naturally utilized.Hierarchical structureshave aninherentcapabilitytocreate more value thanis
captured.The sharingof data across paths alsoincreasesthe numberof possiblequeries.Referencesto
multiple pathsuse powerfulLCA processingtoutilizethiscomplex concurrentmultipathprocessing
furtherenhancingthe semantics.Thisenablesthe abilitytoutilizenode datafromhierarchicallyrelated
pathwaysthatalwaysderivesmeaningful results.

20. Slide20: JoiningHierarchical ViewsDone Same as in Hierarchical Data Modeling
The joiningof hierarchical viewsisalsoperformedinthe exactsame easyway the hierarchical data
model wascreatedshowninthe boxes inthisslide.ThisisbyusingLeftjoinstohierarchicallymodel
structures.Inthisexample,hierarchical viewsABCandXYZare easilyhierarchicallyjoineddynamically
usingLeftjoins.Thisisshowninthe dynamicSELECT statementatthe redarrow. Thisisalso how logical
hierarchical viewsare dynamicallycombinedonthe fly. Thisisperformedwithasimple SQLSELECT
querythat modelsstructuresandjoinsviewsbothinthe same exactway.Thismakesthemseamless
and intuitiveoperationsasshown.

21. Slide21: QueryResult Saved as a Viewfor Reuse inQuerying
The queryresultcan be savedfor reuse infollowingqueriesusingthe SAVEkeyword. “SAVEVIEWas
XYZ” will save the queryasa viewwiththe givenname XYZ. “SAVEDATA as XYZ” will save the queryas
data withthe givenname XYZ. “SAVEDATA …” will preserve the exactdataresultandwill operate asa
view,while“SAVEVIEW…”will save the view whichwillalwaysproduce the mostcurrentresultsof the
view.Eitherone canbe usedanywhere inaquerythat a view canbe used. Asan example,the redarrow
pointstothe combinedview syntaxof the joinof twoview structuresfromapreviousjoin thatcan be
save as DATA or a VIEW.

22. Slide22: DataDrivenHierarchical StructureModeling
Data drivenprocessing isanotherverypowerful additional use of the ON clause thatisnot generally
realized.Itcanbe usedtospecifysimple tocomplexvariable data-drivenbuildingof hierarchical
structures.Itusesa compoundON clause argumentthatteststhe value of storeddata itemstocontrol
the dynamicdata-drivenstructure generation.Thisexample will onlyperformthe joinof XYZtoABC if
the data argumentX=4 isalsotrue. Thisisshowninthe SELECT statementdirectlyabove the redarrow.
Thisalsocan allowmultiple SELECTstobe usedto selecta view fromanumberof manypossible views
dependingonadatabase data value match.Thisis a powerful natural selection capabilitythatis
available touse whenneeded.

23. Slide23: Structure-AwareProcessing ExtendsDynamicUses
EnablingSQLto performmore powerful andextendeddynamiccapabilitiesisanextremelyusefuland
powerful enhancementforSQL.SQL has alwaysbeenadynamiclanguage allowingthe SQLtobe defined
dynamically.Butpreviouslyitcouldnotuse thisdynamiccapabilityanyfurther.AfterSQLhad
dynamicallybeenspecifiedandexecuted,itremainedstatic.Dynamicspecifyingof structurestobe
joinedispossible.Butfurtherdynamicoperationsrequiredmetadataknowledge of the completely
formedstructure thatwas not previously available.Thisnew extendeddynamiclevel of processingin
SQL is nowpossible using anewStructure-Aware processing.

24. Slide24: Structure-Aware Processingfor Dynamic Structures
WithStructure-Aware processingshownatthe redarrow,SQL processingcan be seamlesslyextendedto
the furtherprocessingof dynamicallycreatedstructures.Thisiswhere SQLcancontinue tooperate on
dynamically fully createdstructures.Thistakesintoconsiderationnewcapabilitiesrequiringknowledge
of the dynamicallycreatedstructures.WiththisStructure-aware processing,processingcanbe applied
afterdynamicallycreatedstructuresare fullycreated.Thisextendedstructure-aware processingcan
seamlesslysupportnewinternal andexternal operations inSQL.

25. Slide25: Data Structure Extraction (DSE) ExposesMetadata 4 Use
The dynamicmetainformationrequiredforstructure-awareprocessingisderivedautomatically.With
SQL limitedtousingonlythe Leftjointoperformhierarchically,the SQLcontainsthismetadata
information.Thismeansthe run-time hierarchical SQLLeftouterjoinsyntax atthe redarrow can be
automatically parsed.Thisisperformed bythe new DataStructure Extraction(DSE) processorat the
greenarrow.It will interpretthe dynamichierarchical structure usingthe DSEprocessto parse the Left
joinsandON clausestodynamicallydetermine the datastructure.Thisisthe 3rd of 4 new breakthrough
discoveries.Itenablesstructure-aware processingto greatly extendthe dynamicstructure processingto
unlimitednewandpreviouslyunavailablecapabilities.

26. Slide26: This DSE Enables Powerful NewDynamic Capabilities
The Data Structure Extraction(DSE) syntax parsing at the redarrow dynamicallyconvertsthe combined
inputstructure viewsyntax intometadatarepresentingthe combinedstructure.Thisishandedoff to
the Structure-Aware routinepointedtobythe greenarrow to seamlesslysupplyall the advanced
capabilitiesrequiringthisdynamicinformation.Anexampleuse isthe furtherconvertingof the dynamic
or internal hierarchical structure toexternalformatssuchasXML formattedoutput.Thisrequires
knowledge of the structure metadatasuppliedfromthe Structure-Aware routine.Anotherexample is
supportinghierarchical optimizationwhichalsorequiresknowledge of the structure size andstructure
metadatasuppliedby the newStructure-Aware routine.

27. Slide27: followingSlidesmayUtilize thisNewDynamic Ability
The newcapabilities described inthe followingslidesmayuse the structure-awarecapabilitytosupport
theirnewcapability. Theseslidesmayinherentlyuse the structure-aware processingcapabilitytoenable
advancednewextendeddynamiccapabilitiesautomatically.The structure-aware capability extractsthe
final combined metadatastructure whichisunderthe redarrow as the resulthierarchical structure.The
executingSQLcanutilize thisresultforfurtherprocessing. ThisDSEfinal structure informationwill also
be usedto transformthe final relational structure resulttoa hierarchical multipathresult.Thisadds
considerablytoitsfinal flexibility andfurtheruse.

28. Slide 28: Advanced Hierarchical Data ModelingBreakthrough
The 4th of 4 breakthroughdiscoveriesisthatSQL inherentlysupportslinkinghierarchically anywhere
belowthe lowerlevel structure’sroot.This canbe to anylowerlevel node locationtojoinhierarchical
structures.Anexample isshownatthe redarrow node Z location.Before thisdiscovery,hierarchical
data modelinghadbeenlimitedtoonlylinkingtothe lowerstructure root entry,node Xin thiscase.
Linkingdirectlybelowthe rootcanbe freelyperformed hierarchically.Thisisbecause the rootisalways
the hierarchicallydatamodelledpointof entryshownasX nexttothe greenarrow. Linkingbelow the
root worksinANSISQL because the lowerstructure isfullyconstructedandself-containedbyview
materialization before itislinkedto.Thisis described furtherinthe followingslides.

29. Slide29: PerformsPowerful SemanticallyAccurate Mashups
Linkinghierarchicallydirectlybelowthe rootatthe redarrow meansthatlinking toany node belowthe
root isvalid.Thissignificantlyincreasesthe numberof wayshierarchical structurescanbe linked
together.The upperlevel structure alsohasnorestrictionsfromwhereitcanbe linkedfromaslong as
the paths outare hierarchicallyvalid.Creatingnon-hierarchical structureswill terminatethe current
operation. The newerlowerlevel linkingrequiresnorestrictionstojoininganywhere inthe lower
structure enablingamuchwiderrange of validqueries.Thisoperation alsosupports averypowerful
mashupthat fullymaintainsthe hierarchical semantics naturallyand correctly.

30. Slide30: ProducesExtremelyPrecise SemanticMeaning
Beingable toLink anywhere belowthe lowerlevel structure rootalsoallowsmore precisesemantic
meaninginthe result.Inthisslide,node Cislinkeddirectlytothe lowerlevel structure’snode Zwhichis
at the redarrow.The resultwouldbe semanticallydifferentif ithadbeenlinkedtonode Y at the green
arrow. Thismultiple choiceaddsconsiderablymore accuracyandprecisenessforthe queryandits
processing.Thislevel of automatichierarchicalqueryprecisenesshasnotbeenpossible before.This
precise lowerlevel joiningresultsinthe same datamodelingwhichisalwaystothe lowerlevel root
shownat the blue arrow.Thisoccurs regardlessof whichlowerlevel linkpointwaslinkedtobecause the
root has alreadybeenestablishedas node X.Thisalsoallowsadditional andvariable datafiltering
controlledbythe choice of differentlowerlevel node linkpoints.

31. Slide31: Supports UnlimitedLinkingBelowRoot Capability
Linkingbelowthe rootof the lowerlevel structure XYZrequiresthatitto be fullymaterializedbeforeitis
linkedto.This will treatthe lowerstructure asa solidfullyformedstructure inisolationwithitsown
semanticsalreadyestablished.This causes ittoalwaysbe modelled startingatitsrootby the red arrow
to be semantically accurate whilebeingdirectly joinedtoanynode inthe fullyformedlowerstructure.
Thisenables ittobe data filteredstartingatthislowernode linkpoint node Zatthe greenarrow.This
viewmaterializationinisolationisaccomplishedbyANSISQL’spowerful andflexible outerjoinsyntax
processing.Itisnaturallyperformedasshowninthe nextslide.

32. Slide32: UsesPowerful Little Known Natural SQL ViewSyntax
The SQL inthe box showshowSQL’s Leftjoinprocessingcausesaview’sfull expansionbefore joined.
Thisoccurs in SQL generationproducingmultiple“LeftJoins”withnointerveningON clauses.ThisANSI
SQL syntax naturally producesnestingof viewsonone side,andsequential ON clauseswithno
intervening“Join”onthe otherside causingun-nesting.This triggersthe full expansionof view XYZin
boldat the blue arrow before itisjoinedtoview ABC.Thisnestingis natural withviewexpansion shown
at the greenarrow pointingtothe SQL expandedsyntax:“LEFTJOIN XLEFT JOIN Y“and endingwith this
syntax:“ON X.x=Z.zON C.c=Z,z. Thisview expansionoccursnaturally inthe expandedboldsyntax atthe
blue arrow provingthissyntax naturallyoccurs andexecutescorrectly.ThisdelaysjoiningviewXYZto
view ABCuntil viewXYZisfullyexpanded. Thisseamless capability makesviews more powerful and
easiertouse.

33. Slide33: Remote HeterogeneousInputAccess& Processing
The red arrow inthisslide pointstoviewXYZwhichinthisexample representsaremote XML view.Itis
retrievedand heterogeneously combinedtransparentlyandseamlesslywiththe SQLhierarchical ABC
viewshownbythe greenarrow.Thisenablesintroducingdatafromremote locationsseamlesslysuchas
XML andcombiningitheterogeneouslywithSQLsource.Thisispossible andseamlessbecauseXMLis
alsohierarchical. The XML definitionpointedtobythe blue arrow inthe lowerbox requiresamore
specifichierarchical definitionasshown.Thisisbecause the XML definitionisexternal andrequires
additional dataspecifictoXML to be made.The hierarchical structure inthe XML definitionisdefinedby
the Parentkeywordsindicatedbythe doublepointedpurplearrow. Thismayrequire furtherSQL
additionstohandle the differenttypesof remote hierarchical databases.

34. Slide34: SimpleSpecifications NaturallyControlProcessing
Usingthe ANSISQL SELECT listat the greenarrow,onlythe data itemsto be retrieved,circledinred
(A.a,B.b,D.d),needtobe specified.Theyare specifiedinanyorderwithnochange in result.A change in
processingonlyrequiresaddingorremovingdataitemsinthe SELECT list.The SELECT’sFROM clause
generatesthe hierarchical datamodel tobe semanticallyfollowedandinvokesthe SQLatthe red arrow.
Thisis furtherprocessedif multipathconcurrentprocessingisperformedusingthe WHEREclause to
make the cross-pathconnections.Thisisperformedbythe inherentrelational Cartesianproductandits
natural LCA processingproducingthe resultshownbythe blue arrow.Thisishow the data SELECT list,
FROMclause and WHERE clause naturallycontrolscomplex processingeasilyandaccurately.

35. Slide35: Hierarchical OptimizedData Access withNode Removal
Usingthe SQL hierarchical SELECTlistoperationatthe greenarrow,it can be automatically determined
whichnodesare outside the hierarchical range of the active query.These nodes willnotrequire
accessing.Theyare removedfromconsiderationbefore queryprocessingstarts.Thishierarchical
optimizationisshowninthisslide wherenode Eis not referencedandisoutof range,so itis not
accessed.Thisisindicatedbya slashthroughnode E whichispointedtoby the redarrow. This
hierarchical optimizationcanalsoincrease the efficiencyandeffectivenessof the standardrelational
optimizationthatfollows.Thisisbecause ithasreduced the required relationaloptimizationbymaking
it simplertoprocessandmore effective.

36. Slide36: Automatic Data Aggregationwith Node Promotion
WithSQL’s non-procedural SELECTlistprocessingatthe greenarrow;automaticdata aggregation,node
promotionand node collectionare performedbyonlyspecifyingwhichdatatypesare tobe retrieved.
Thisis showninthisslide’sresultpointedtobythe red arrow where node C wascompressedout
betweennodesA andD.Thishappensbecause itwasnot referenced,butitisstill requiredfor internal
navigationfromnode A to node D.In relational databasesthisremovalis causedby relational
projection. Inhierarchical processing,thisremoval iscallednode promotion.Withnode promotion,the
remainingoutputnodesare collected hierarchically togetherautomaticallyproducinganicely
aggregateddataresult.

37. Slide37: EnablesGlobal Views,EasierTo Use,Has No Overhead
Withhierarchical optimizationbeingautomaticallyperformedineachview,hierarchical viewsbecome
global views by alsosupportingsubsetsof the global view.They canhandle more thanone view cutting
downon the numberof viewsnecessary.Thismeansagivenglobal view canservice more thanone
queryafterthe viewisoptimized.Thisreducesthe numberof differentviewsnecessary,whichmakes
queryingmucheasier, automaticandefficientforthe user.Withhierarchical optimizationalways
operating,there isnooverheadforglobal views.Thisis because eachqueryonlyaccessesthe datait
needsto.

38. Slide38: Allowsan Infinite Numberof Dynamic NewCapabilities
The natural powerof the SQL data SELECT controllinginternal processingcombinedwiththe additionof
structure-aware processingcanenable aninfinite numberof new capabilities. Forexample,thiscan
supportSQL transparenthierarchical XML processingof inputanddynamicallycreatedoutputasfully
formattedXML. Thisoccurs afterthe dynamicstructure isgenerated. Thishasbeendone andisshown
on the followingslide. Thisenablesunlimitednew capabilities.

39. Slide39: NewDuplicate Data Type FixesReplicatedData Problems
Joiningrelational tablesusuallyproducesthe relationalCartesianproductwhichexplodesdatainserting
replicateddataasplace holdersformissingrow matches.Thisaddssevere inefficienciesandcancause
problemswhenremovingreplicateddatawhenthere isduplicate data.Thisisbecause the duplicate
rowsmay be removedwhentheyshouldbe preserved.The duplicatedatatype solutionabove works
seamlesslybysupportingbothduplicate dataandreplicateddata totell themapart.Thisrequires
internal additionstoSQL tokeeptrack and separate real datafrom duplicate databytaggingit.The
duplicate datatype alsodecreasesunnecessarydatareplicationfurtherincreasinghierarchical
optimizationalreadydescribed.Thisreducingof the replicateddataalsoincreasesaccuracyand
correctness.

40. Slide40: Hierarchical SQL Transparently Supports XML
Thisslide showsthe SQLSELECT statementusedtoproduce the automaticallyformattedhierarchical
XML pointedtobythe red arrow.This ispossible becauseSQLhierarchical processingcansupport
dynamicandautomaticstructuredXML formattedI/O.Thisusesstructure-aware processingtoknow
howto format the XML from the final physical hierarchical structure result.The unambiguousmultipath
structureddata alsoenablesnavigationless,schema-free XMLaccess.Notice thatthe node promotion
causedthe unreferencedCustandEmpnodes nexttothe greenarrowsare correctlyslicedoutintheir
dynamicallyproducedXML.Thisproducesanicelyaggregatedresult.Thiscansupportanyhierarchical
structure such as IBM’s IMS database.

41. Slide41: SQL/XML Std Has Hierarchical Inner JoinProblems
Secretagendasandpoliticskeptthe Innerjoinasthe defaultjoinforthe SQL/XML Standardand XQuery.
The designers believedthiswouldmore easilyleadthe wayfromSQLto XML. This wasa terrible
decision,becausethe InnerjoindoesnotsupporthierarchicalstructureslikeXML.Infact it destroys
themturningthemintoflatstructures.The SQL/XML Standarddesignerswanted tomove beyondSQL
and replace SQLwithXQuery.Theythoughtkeepingthe InnerjoinwouldhelptransitionfromSQLto
XQuerybykeepingthe familiarInnerjoin.Ihave some knowledge andinsightintotheseproblems
havingbeenone of the initial membersto the SQLXGroup workingonthe SQL/XML Standard.These
decisionshave causedthe problemsdiscussedinthe followingslides

42. Slide42: SQL/XML Std RequiresProcedural Code & Navigation
The SQL/XML Standardrequiresprocedural code andusernavigationfor accessingXMLfromSQL. Thisis
because itsupportssemi-structureddatarequiringusernavigation.Semi-structureddatarequiresuser
navigationbecause anode type canbe locatedfrommore thanone path, eachhas a different
semantics.The newSQLhierarchical navigationlessaccessusesonlystructureddatawithsinglepathsto
each node type. Itdoesnotneedto be usernavigatedbecause the structure isunambiguousenabling
automaticnavigation.Forthese reasons, the automatichierarchical SQLXML supportisconsistently
accurate and correct forstandardstructuredSQL and can be seamlesslyextendedtoall other
hierarchical languages.Onthe otherhand, the SQL/XMLsemi-structuredStandardwithmultiplepaths
to nodesrequiresusernavigation.Thisisbetterforunderstandingandusingunstructureddata. Both
wayshave theirgood and bad points.

43. Slide43: SQL/XML Std Doesn’tSupport Automatic LCA Logic
Finally,there wasafailure tosupportautomaticLCA processingbyXQuery.Eventryingtouse a
specializedLCA functiondidnotworkwell andoftenenough.LCA processingisextremelycomplex and
impractical tocode byhand.On the other hand,ANSISQLcan naturallyandautomaticallysupportfull
LCA multipath processing.ThisincludesXMLkeywordsearchusingSQL.Thishas now beenutilizedin
hierarchical SQL’snewlydiscoveredinherentmultipathhierarchical processingcapability. This
significantlysynergizesthiscombinationandintegrationof relational andhierarchical processing’snew
semanticprocessing capabilitiesof.

44. Slide44: Hierarchical SQL Also SupportsMultipath Ordering
Rowsin ANSISQLare unordered andflatwhile XMLisorderedand supportsmultiple pathprocessing.
So SQL hierarchical processing doessupportorderingof multipathprocessing.Because of this,the XML
inputorderand multipathprocessing ispreservedinSQLhierarchical processing.Notice inthe diagram
that the Invoice andEaddr data typesare independentlyorderedontheir differentpathsatthe green
arrows. Theirseparate data occurrencesare pointedtobythe redarrows.This multipathorderingcan
alsobe usedtoperformmultipathsummaries. The XMLquery above producesthe XML outputshown
whichwasproducedfromthe SQL hierarchical processor.Itcontainsthe ordering capability.Multipath
aggregatesandsummariescouldalsobe supported inthe same way.

45. Slide45: SeamlessPeer-to-PeerReal-TimeAutomaticMetadataMaintenance
Peer-to-peerprocessingsupports global concurrentmulti-pathSQLmetadata:communication,design
and coding.ThisallowsSQLdesignandcodingto be performedcollaborativelytobuildandtestSQLin
real-time.Inthe example shown,P1forpeer1starts thiscollaborative SQLoperationinputtingand
combiningof separate relational tablesA,B,C and the fixedhierarchical structure XYZ shown bythe
greenarrows. P1 passesthemto separate pathsP2 and P3 at the purple arrowsforseparate processing
that buildsthe SQLin parallel.This proceeds until the two differentpathsare joinedcombiningthe two
SQL structuresintoa single SQLresult at P4 by the red arrows.The final SQL source at P4 is shownat the
blue arrow.Transparentlysupportingthe entire P-to-P metadataprocessingautomatically isseamlessly
performedby the new AutomaticMetadataMaintenance.Thishidesall globalmetadataprocessing
fromthe user.

46. Slide46: Connecting UnrelatedStructures
ViewsCustView andEmpView fromdifferentstructures atthe blue arrows have no directrelationships
intheirdata values.They canstill be relatedthroughasimple relational associationtable that supplies
the needed relationships.Anadvantage of thisassociationtable isthatMto M relationshipslike Parts
and Supplierscanbe defined andusedfromeitherdirection.Thismeanseithersuppliersorpartscould
be on top.M to M relationshipsare appliedas1to M relationshipson topand the matchingM to 1 on
the bottom.Thisalsoallowsforthe addition of intersectingdatato be stored inthe associationtable
that isdifferentforeachmatchingrelationship.Inthisexample this isthe specificcustomer/employee
associateddatacombinationfoundinthe intersectingdatacolumn pointedtoby the red dashedarrow.

47. Slide47: AdvancedStructureTransformationsinTest
Evenwithall the relational discoveriesandtheiradvance new capabilitiesalreadyshown,we are still
pursuingandresearchingnewadvancedcapabilitieslike those shown onthisslide.These include
dynamicstructure transformationsthatallow dynamicallyandflexiblychangingthe datastructure as
needed.They use differentandnewrelationships torestructure the data. Thisalsoincludesour
powerful newdatastructure reshaping capability.Itusesthe existingsemanticstoreshape the data
structure inany way dynamicallywhilepreservingthe semantics.Eachof these restructuringmethods
has itsownspecificuses,andbothmethodscanbe usedtogether.

48. Slide48: NewSemanticSQL is More Efficient
StandardSQL producesa flatstructure withno semanticsproducedbyCartesianprocessing keepingit
inefficient.Efficiencyisthe ratioof powersuppliedtoworkperformed.Increasingworkperformed
withoutincreasingpowersuppliedincreasesefficiency. The new semanticSQLhierarchical processing
significantlyincreasesSQLprocessingnaturallyutilizingthe LeftJoin generatedsemanticsproducinga
higherperformance.Besidesthispowerful semanticsusage there are twootherareaswere semantics
come intoplayincreasingefficiency.These are fixedsemanticsinhierarchical structures anddynamic
semanticswhere hierarchical structuresare joined increasingsemantics.Allof these differentsemantics
can buildoneach otherto supporta significantly higherperformancemultipathengine byincreasing
efficiencywithoutincreasingpowersupplied usedtoproduce aleapinanalytical andcomplex
processing.

49. Slide49: Relational Discoveries ProofofConcept
All of the newANSISQL hierarchical processingcapabilitiesshownhave beensupportedinour
functioningprototypeshownbelow.ThisbreakthroughmultipathSQLnatural hierarchical processorand
technologyhasbeenimplementedandtested.Itisoperatingfullyonanintegrationof relational algebra
and hierarchical principlesthathave beenmathematicallyandlogicallyprovento existandfunction
togethersynergistically. Thisnew SQLnow includes manycapabilities thatwere outside the current
domainof SQL but are nowwithinitbecause of the native relational hierarchical processing.
One final deeperexplanation andproof of LCA operationshowninthispresentationthat demonstrates
and proveshowandwhyit works is mypaper:The PowerBehindSQL's InherentMultipathLCA
Hierarchical Processingat: http://www.databasejournal.com/features/article.php/3882741/article.htm
See the SQL multipathhierarchical processorinaction fromactual processing outputfromanearlier
versionat:http://www.adatinc.com/images/Verifying_SQLfX_Current.pdf
My newbook AdvancedStandard SQL Dynamic Structured Data ModelingandHierarchical Processing
fromArtechHouse Publishers describesmanyof the capabilitiesdescribedinthispresentationinmore
detail.Thisnewbook canbe foundat: http://www.artechhouse.com/Main/Books/Advanced-Standard-
SQL-Dynamic-Structured-Data-Mode-2071.aspx
Anycompanyhavingan interestoruse for thispowerful new breakthroughanddisruptive semanticSQL
querytechnology andproductcan contact Mike at: mmdavid@acm.org.

50. Slide50: SQL CHALLENGE
I will sendacopy of mynewbook: Advanced Standard SQL Dynamic Structured Data Modelingand
Hierarchical ProcessingfromArtechHouse Publisherstothe firsttwopeople thatfindanuncorrectable
error inthe newSQL processinglogic(syntax,semantics, operation) Iampresentinghere.Describe the
SQL error foundor questionyouhave andspecifyyouremail.See thisnew bookat:
http://www.artechhouse.com/Main/Books/Advanced-Standard-SQL-Dynamic-Structured-Data-Mode-
2071.aspx