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The Most Powerful and Accurate SQL

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Four new ANSI SQL relational processing discoveries and their 15 breakthrough capabilities, fundamental principles, and operation are explained in this presentation. These capabilities add powerful new operations while eliminating problem SQL areas. The first discovery supports advanced capabilities through the natural integration of multipath hierarchical processing into the front end of the relational processing. This supports relational processing in a powerful hierarchical multipath nonlinear fashion. This is both relationally sound and hierarchically principled using standard SQL syntax. On top of this SQL hierarchical processing model, a network structure is controlled by dynamic referencing data items across paths.

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The Most Powerful and Accurate SQL

  1. 1. Slide1:TheMostPowerful andAccurateSQL 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 referencingany dataitems concurrently acrossanddownpathssolvingthe queryfrommultiple directionsproducinganewleapinanalysis.Thisusesasecondpowerfulprocessingdiscovery of inherentLCA processingthatnaturally determines andprocesses the semanticmeaningacrosspathways naturally.These capabilities produce anextremely advancednew generationof powerful SQLsemantic operation.
  2. 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 usesandcapabilitiesnot previouslyavailable 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. 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 thenalsobe usedto reference dataitems acrossthe datamodel pathwaysina powerful networkfashion.
  4. 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 semanticprocessinglevel.Thisisbecause the semantics are knownfromthe datamodelingalready appliedandcan be internallyutilized.
  5. 5. Slide5:Hierarchical Processingis Usedin Relational Processing The inherenthierarchical processingpossible instandardSQLisan importantanduseful basicdiscovery. It has shownthatpowerful hierarchical processingisa validsubsetof relational processingandcan be usedforcomplete natural integration.Thisalsoallowsrelational dataindependenceandhierarchical data modelingtonaturallycombinethe advantagesof both.The redarrow pointsto the box that uses onlyLeftouterjoinsoperationstoenable powerful hierarchical multipathdatamodelingandprocessing. The hierarchical processingfollowsnatural datapreservationprinciples.Thisaddstothe inherent correctnessalongwithrelational processing’spowerful mathematical foundation.Thisproducesanew semanticSQLoperatingat a much higher processinglevel.Itnaturally interprets hierarchical data modelingfromitssyntax.Thisenables hierarchical processingthatnaturallyutilizesthe new SQL hierarchical semantics betweenandacrosspathways.Thisalsomeansthatthere isno new code to support.
  6. 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’sstandardnavigationlessaccess.
  7. 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. 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 dutiesmakeseachoperationmore powerful anddistinct.
  9. 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 tag.
  10. 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 meaningful query result.
  11. 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 occurrencesE3andE4 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. 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 connectingdataandusingthe semantics acrosspathways. ThisisknowntechnicallyandacademicallyasLowestCommonAncestor (LCA) processingwithitsnew use now performedby SQLmultipathhierarchical processing.This concurrentmultipathhierarchical processing showninred canbe furtherenhanced byreferencing across active pathwayswhich utilizes LCA processing.Thisgreatlyincreases the analytical processing capabilitiesinnew,more:meaningful,accurate andpowerful ways by utilizingconcurrentmultiple active and connectedpathwaysthatcansupportpowerful networkstructures.
  13. 13. Slide13: InherentLowestCommon Ancestor (LCA) MultipathProcessing The 2nd of 4 relational breakthroughdiscoveriesisthe verypowerfulLCA processingfoundoperating naturallyandinherentlyinANSISQL.Olderphysical hierarchical structuresrequiredacomplex search for LCAs.For example,the datareferencesB,D& E showninred arrows wouldhave requiredsearching upwardsfromthe referencednodesB,D and E to locate LCA nodesC andthenA. But SQL hierarchical LCA processingisoccurringinherentlyinSQLrequiringnosearchingorcodingat all.Thisnatural LCA processingutilizesthe relationalprocessing’sCartesianproduct’soperation. The generatedCartesian productcontrollingthe searchupto the LCAs isshown in red.LCAsare at the connectionpointwhere the pathways meet.Thisnatural andcomplex operationenablesLCA’soperationtoany nestinglevel. ThisLCA processingis necessary because the required dynamicLCA codingwould become very complex and costly to code by hand as shownlater.WithSQL inherentlyperformingall the LCA processingitis alwaysoperatingcorrectlyandefficiently.
  14. 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 LCA processingbetweenthe pathwaysutilizesthe implied semanticsbetweenthe pathways. The Cartesianproductdataaroundan LCA extends naturally toitslowestnode references, DandE for node C and thenB and C for node A as shownonthis slide.
  15. 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 occurringinherently. Iwas notaware of thisLCA operationoccurring inherently until Irealizedsomething hadtobe naturallycausingit because the multipathresultswere alwayscorrect.I found thisnatural LCA processingin the Cartesianproduct controlled withinthe initial hierarchical processing model.More onhow andwhy thisworksnaturallycanbe foundon slide #49.
  16. 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 extremelypowerful WHEREclause reference using AND,OR and parenthesis operationslinkingthemtogetherinanyway. Thisenablesall connectionsto be testedbecause everynode canreferenceeveryothernode shownbythe blacklinesoverthe white lines. Thissupports an extremely powerful networkeddataanalysis 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. 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. Theyare more powerful thanbefore withthe discoveryof SQLinherent multipathhierarchical processinginrelational databases. Withlogical hierarchical processing,multipathhierarchical structures and viewscanbe hierarchicallycombineddynamicallyinanyorderandthenprocessed.These new powerful logical structuresare alsoveryefficientbecause theyare temporaryandnaturallyfreed-up afterthe querycompletes. Thisnew SQLhierarchical processing canbe fullydynamicandlogical.This adds significantflexibility increasinganalysis.
  18. 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 navigationless schema-free navigation regardlessof how the final heterogeneous hierarchical structure iscomposed.
  19. 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.Thisiswhyhierarchical structureshave aninherentcapabilitytocreate more value than iscaptured.The sharingof data across paths alsoincreasesthe numberof possiblequeries. References betweenmultiple pathsuse powerfulLCA processingtoutilize thiscomplex concurrent multipathprocessingfurtherenhancingthe semantics.Thisenablesthe abilitytoutilize node datafrom hierarchicallyrelatedpathwaysthatalwaysderivesmeaningful results performedbythe LCA processing.
  20. 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.Inthis example,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. 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 savedas DATA or a VIEW.
  22. 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. 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 the newStructure-Aware processing.
  24. 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 seamlesslysupport unlimited new internalandexternal operations inSQL.
  25. 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) processor at the greenarrow.It interpretsthe 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. 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. 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. 28. Slide 28: Advanced Hierarchical Data ModelingBreakthrough The 4th of 4 breakthroughdiscoveriesisthatSQL inherentlysupportslinkinghierarchically anywhere belowthe lowerlevel structure’sroot.Thiscanbe to anylowerlevel node locationtojoinhierarchical structures.Anexample is shownatthe 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. 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. 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 addingsignificantflexibility.
  31. 31. Slide31: Supports UnlimitedLinkingBelowRoot Capability Linkingbelowthe rootof the lower level 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. 32. Slide32: UsesPowerful Little Known Natural SQL ViewSyntax The SQL inthe box showshowSQL’s Leftjoinprocessingcausesaview’sfull expansionbefore Ibeing joined.Thisoccursin SQL generationproducingmultiple “LeftJoins”withnointerveningON clauses. ThisANSISQL 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 as shownat the greenarrow pointingtothe SQL expandedsyntax:“LEFTJOIN X LEFT JOIN Y“andending withthissyntax:“ON X.x=Z.zON C.c=Z,z. Thisview expansion shown occursnaturally inthe expanded boldsyntax at the blue arrow provingthissyntax naturallyoccurs and executescorrectly.Thisdelays joiningview XYZtoview ABCuntil viewXYZisfullyexpanded. Thisseamless andlittleknown natural capability makesviews more powerful andeasiertouse.Ihave neverseenthisnatural syntax operation and itssignificantnew use, flexibilityandcapability documentedanywhere. Havingbeendocumented and itsoperationdescribedhere,itcannow be usedsafely.
  33. 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 requires amore 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 butthe remote heterogeneous capabilityisalreadyinplace.
  34. 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 naturally come togethertoveryeasilyandpowerfully controlscomplex processingeasilyandaccurately.
  35. 35. Slide35: Hierarchical OptimizedData Access withNode Removal Usingthe SQL hierarchical SELECTlistoperationatthe greenarrow, and the structure-aware capability alreadycovered, itcanbe automatically determinedwhichnodesare outsidethe hierarchicalrange of the active query.These nodes willnotrequire accessing.Theyare removedfromconsiderationbefore queryprocessingstarts.Thishierarchical optimizationisshowninthisslidewhere nodeEisnot referencedandisoutof range,so it isnot accessed.Thisisindicatedbyaslashthroughnode E whichis pointedtobythe redarrow. Thishierarchical optimizationcanalsoincrease the efficiencyand effectivenessof the standardrelational optimizationthatfollows.Thisisbecause ithas increased the required relational optimizationbymakingitsimplertoprocessandmore effective.
  36. 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 node Cwasnot referenced,butit isstill requiredfor internal navigationfromnode A tonode D.In relational databasesthisremoval is calledrelational projection.Inhierarchical processing,thisremoval iscallednode promotion.Withnode promotion,the remainingoutputnodesare collected hierarchically togetherautomaticallyproducinganicely aggregateddataresult.
  37. 37. Slide37: EnablesGlobal Views,EasierTo Use,Has No Overhead Withhierarchical optimizationbeingautomaticallyperformedineachview,hierarchical views become 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. 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 transparent processingof any hierarchical operation suchas performingXMLor evenIBM’s still used hierarchical processing,XMLaccesswill be shownlater.Anotherexampleissupporting hierarchical optimization.These are possible because these operationsoccurafter the dynamic structure isgenerated andknown.Thisenables unlimitednew capabilities usingstructure aware processingaspreviouslydescribed.
  39. 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 dataandreplicateddatatotell themapart.Thisrequires internal additionstoSQL tokeeptrack and separate real datafrom duplicate databytaggingit.The duplicate datatype alsodecreasesunnecessarydatareplicationfurtherincreasinghierarchical optimization efficiency alreadydescribed.Thisreducingof the replicateddataalsoincreasesaccuracy and correctness.
  40. 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. 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 Innerjoinwouldhelp the transitionfromSQLto XQuerybykeepingthe familiarInnerjoin.Ihave some inside knowledge andinsightintothese problems havingbeenone of the initial memberstothe SQLXGroup workingonthe SQL/XML Standard.These decisionshave causedthe XMLproblemsdiscussedinthe followingslides
  42. 42. Slide42: SQL/XML Std RequiresProcedural Code & Navigation The SQL/XML Standardrequiresprocedural code anduser navigationforaccessingXMLfromSQL. 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 automaticfully hierarchical SQLXML supportisconsistently accurate and correct forstandardstructuredSQL and can be seamlesslyextendedtoall other hierarchical languages.Onthe otherhand, the SQL/XMLStandardsemi-structured processingwith multiple pathstonodes requiresusernavigation.Thisisbetterforunderstandingandusing unstructureddata. Both wayshave theirgood and badpoints and shouldhave beenkept separate.
  43. 43. Slide43: SQL/XML Std Doesn’tSupport Automatic LCA Logic Finally, withthe SQL/XMLStdsupportingthe Innerjoin asexplainedpreviously, there wasafailure to supportautomaticLCA processingbyXQuery.Eventryingtouse a specializedLCA functiondidnotwork well andoftenenough.LCA processingisextremelycomplexandimpractical tocode byhand.On the otherhand,ANSI hierarchically structured SQLcannaturallyandautomaticallysupportfullLCA multipathprocessing.ThisincludesXMLkeywordsearchusingSQL.Thishas now beenutilizedin hierarchical SQL’snewlydiscoveredinherentmultipathhierarchical processingcapability. This significantlysynergizes thiscombinationandintegrationof relational andhierarchical processing’snew semanticprocessing capabilitiesof hierarchical SQL.
  44. 44. Slide44: Hierarchical SQL Also SupportsMultipath Ordering Rowsin standard ANSISQL are unordered andflatwhile XMLisorderedandsupportsmultiplepath processing.The standardANSI SQLprocessingdoessupportordering of multipathprocessing.Soitwas supportedinhierarchical multipathSQL. Thisallowsthe XMLinputorderand multipathprocessing to be preservedinSQLhierarchical processing.Notice inthe diagramthatthe Invoice andEaddr data types are independentlyorderedontheir differentpathsatthe greenarrows. Theirseparate dataoccurrences are pointedtobythe red arrows.Thismultipathorderingcan alsobe usedto performmultipath summaries. The XML queryabove producesthe XML outputshownwhichwasproducedfromthe SQL hierarchical prototype processor.Itcontainsthe ordering capability.Multipathaggregatesand summariescouldalsobe supported inthe same way.
  45. 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 a new AutomaticMetadataMaintenance.Thisautomatically hidesall global metadata processingfromthe user.
  46. 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 storedinthe associationtable that isdifferentforeachmatchingrelationship.Inthisexample this isthe specificcustomer/employee associateddatacombinationfoundinthe intersectingdatacolumn pointedtoby the red dashedarrow.
  47. 47. Slide47: AdvancedStructureTransformationsinTest Evenwithall the relational discoveriesandtheiradvance new capabilitiesalreadyshown,we are still pursuingandresearchingnewadvancedcapabilitieslike those shown onthisslide.These include dynamicstructure transformationsthatallow dynamicallyandflexiblychangingthe datastructure as needed dynamically.They use differentandnew relationships torestructure the data. Thisalsoincludes our powerful new dynamicdatastructure reshaping capability. Thisusesthe existingsemanticsto reshape the datastructure inany waydynamicallywhile preservingthe semantics.Eachof these dynamicrestructuringmethods hasitsownspecificuses,andbothmethodscanbe usedtogether.
  48. 48. Slide48: NewSemanticSQL is More Efficient StandardSQL producesa flatstructure withno semanticsproducedbyCartesianprocessing keepingit inefficient.Efficiencyisthe ratioof powersuppliedtoworkperformed.Increasingworkperformed withoutincreasingpowersupplied isperformedby increasesefficiency. The new semanticSQL hierarchical processingsignificantlyincreasesSQLprocessingby naturallyutilizingthe LeftJoin’simplied semanticsproducingahigherperformance.Besidesthispowerfulsemanticsusage there are twoother areas were semanticscome intoplayincreasingefficiency.These are fixedsemanticsinhierarchical structures and dynamicsemanticswhere hierarchical structuresare joined increasingsemantics. All of these differentsemanticscanbuildoneachotherto supporta significantly higherperformance multipathenginebyincreasingefficiency toproduce aleapinanalytical andcomplex processing. The bestincrease insemanticsisthe powerfuldynamicnatural semanticgenerationcreatedfrommultipath processingwhichisusedforpowerful LCA processingbetween pathways.
  49. 49. Slide49: Relational Discoveries ProofofConcept All of the newANSISQL hierarchical processingcapabilitiesshownhave beensupportedinour functioningprototypelistedbelow.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 new book 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. 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

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