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JUDCon  India 2012 Drools Expert
 

JUDCon India 2012 Drools Expert

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Drools Expert talk given at JUDCon India 2012

Drools Expert talk given at JUDCon India 2012

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  • JBoss Enteprise BRMS (new in 2009) Enables critical business rules to be managed in a more centralized manner (e.g. Insurance = policy risk assess and pricing; Heathcare = claims processing annual regulatory changes) Avoids need to otherwise re-code business rules redundantly in multiple applications Leverages JBoss Rules execution engine which has been available for years Adds new browser-based Rules Mgmt app, enabling business users to participate in the review, editing, and maintenance of business rule changes Also adds Repository to provide version management of multiple sets of business rules Supports the deployment of business rules to JBoss middleware platforms and non-JBoss runtime environments
  • JBoss Enteprise BRMS (new in 2009) Enables critical business rules to be managed in a more centralized manner (e.g. Insurance = policy risk assess and pricing; Heathcare = claims processing annual regulatory changes) Avoids need to otherwise re-code business rules redundantly in multiple applications Leverages JBoss Rules execution engine which has been available for years Adds new browser-based Rules Mgmt app, enabling business users to participate in the review, editing, and maintenance of business rule changes Also adds Repository to provide version management of multiple sets of business rules Supports the deployment of business rules to JBoss middleware platforms and non-JBoss runtime environments

JUDCon  India 2012 Drools Expert JUDCon India 2012 Drools Expert Presentation Transcript

  • 1 Mark Proctor Project LeadThe SkyNet funding bill is passed.The system goes online on August 4th, 1997.Human decisions are removed from strategic defense.SkyNet begins to learn at a geometric rate.It becomes self-aware at 2:14am Eastern time, August 29thIn a panic, they try to pull the plug.And, Skynet fights back
  • 2Wumpus World
  • 3Wumpus World
  • 4Wumpus WorldC e ll H e ro Wu m p u s P itt G old int row int row int row int row int row Int col Int col Int col Int col Int col
  • 5Wumpus World
  • 6Wumpus World
  • 7Wumpus World d e m ons tration
  • 8Drools B ooks
  • 9S ample Indus tries and Us ers Inve s tm e nt Mille nniu m Inve s tm e nt G rou p (M IG ) Logis tics Fed ex Airline S ab re M ortgage F ranklin Am e rican H e alth care OSDE
  • 10B oot C amps S an F rancis co 2009 (40+ atte nd e e s ) S p ons ore d b y Th ird P illar S u n, F AM C , O S D E , Kas e ya, F e d e x, TU G rou p , Inte rm ou ntain H e alth care , G ap , S ony P ictu re s , Lockh e e d Martin, Kais e r, H P , We lls F argo, U S N avy R e s e arch , F O LIO fn, Boe ing ..... S an D ie go 201 0 (80+ atte nd e s s ) S p ons ore d b y U S N avy 5 d ay e ve nt, with 2 d ays focu s on th e h e alth care ind u s try O S D E , AT&T, S AIC , U S N avy R e s e arch , Kais e r, C linica, Inte rm ou ntain H e alth care , G E H e alth care , VA, Boe ing, N ationwid e ....
  • 11Integrated S ys tems Rules Rules Workflows Workflows Event Processes Semantic Ontologies Semantic Event Ontologies Processes
  • 12generic Rules and proces s es ? Decision ServicesSCOPE Processspecific Rules tightly coupled COUPLING loosely coupled
  • 13Integrated S ys tems Drools JBPM5 Drools Drools Expert (Drools Flow) Fusion Guvnor Drools Drools Drools Drools Planner Grid Semantics Chance Business Logic integration System
  • 14 Declarative ProgrammingP rod u ction R u le S ys te m s P R D (forward ch aining) R e active wh e n Alarm ( s tatu s = = “ale rt” ) th e n s e nd ( “warning” )Logic P rogram m ing LP (b ackward ch aining) Q u e ry d e s ce nd ant( “m ary”, “j ”) aneF u nctional P rogram m ing F P Map ,F old , F ilte r avg([1 2, 1 6, 4, 6]) R e tu rns s ingle valu e 9.5 rou nd ([1 0.3, 4.7, 7.8] ) R e tu rns Lis t [1 0, 5, 8]D e s crip tion Logic P e rs on H as N am e and
  • 15 C las s es C a s h f lo w A cco u n t D a te d a telo n g a c c o u n t N o d o u b le a m o u n td o u b le b a la n c e in t t y p e lo n g a c c o u n t N o A c c o u n t in g P e r io d D a te s ta r t D a te e n d
  • 16 C redit C as hflow Rule select * from Account acc, Cashflow cf, AccountPeriod ap where acc.accountNo == cf.accountNo and cf.type == CREDIT cf.date >= ap.start and cf.date <= ap.end trigger : acc.balance += cf.amountrule “increase balance for AccountPeriod Credits” when ap : AccountPeriod() acc : Account( $accountNo : accountNo ) CashFlow( type == CREDIT, accountNo == $accountNo, date >= ap.start && <= ap.end, $ammount : ammount ) then acc.balance += $amount;end
  • 17 Rules as a “ view” CashFlow AccountingPeriod date amount type accountNo start end 12-Jan-07 100 CREDIT 1 01-Jan-07 31-Mar-07 2-Feb-07 200 DEBIT 1 18-May-07 50 CREDIT 1 Account 9-Mar-07 75 CREDIT 1 accountNo balance 1 0rule “increase balance for AccountPeriod rule “decrease balance for AccountPeriod Credits” Debits” when when ap : AccountPeriod() ap : AccountPeriod() acc : Account( $accountNo : accountNo ) acc : Account( $accountNo : accountNo ) CashFlow( type == CREDIT, CashFlow( type == DEBIT, accountNo == $accountNo, accountNo == $accountNo, date >= ap.start && <= ap.end, date >= ap.start && <= ap.end, $ammount : ammount ) $ammount : ammount ) then then acc.balance += $amount; acc.balance -= $amount;end CashFlow end CashFlow date amount type date amount type 12-Jan-07 100 CREDIT 2-Feb-07 200 DEBIT 9-Mar-07 75 CREDIT Account accountNo balance 1 -25
  • 18 Definitionsp u b lic clas s Ap p licant { p rivate S tring nam e ; p rivate int age ; p rivate b oole an valid ; / ge tte r and s e tte r m e th od s h e re /} rule "Is of valid age" when $a : Applicant( age < 18 ) then modify( $a ) { valid = false }; ends
  • 19 B uildingKnowle d ge Bu ild e r kb u ild e r = Knowle d ge Bu ild e rF actory.ne wK nowle d ge Bu ild e r();kb u ild e r.ad d ( R e s ou rce F actory .ne wC las s P ath R e s ou rce ( "lice ns e Ap p lication.d rl", ge tC las s () ), R e s ou rce Typ e .D R L );if ( kb u ild e r.h as E rrors () ) { S ys te m .e rr.p rintln( kb u ild e r.ge tE rrors ().toS tring() );}kb as e .ad d K nowle d ge P ackage s ( kb u ild e r.ge tKnowle d ge P ackage s () );
  • 20S pring C onfiguration
  • 21 E xecuting rule "Is of valid age" when $a : Applicant( age < 18 ) then modify( $a ) { valid = false }; endsS tate le s s K nowle d ge S e s s ion ks e s s ion = kb as e .ne wS tate le s s Knowle d ge S e s s ion();Ap p licant ap p licant = ne w Ap p licant( "M r Joh n S m ith ", 1 6 );as s e rtTru e ( ap p licant.is Valid () );ks e s s ion.e xe cu te ( ap p licant );as s e rtF als e ( ap p licant.is Valid () );
  • 22 Definitionsp u b lic clas s R oom { p rivate S tring nam e / ge tte r and s e tte r m e th od s h e re /}p u b lic clas s S p rinkle r { p rivate R oom room ; p rivate b oole an on; / ge tte r and s e tte r m e th od s h e re /}p u b lic clas s F ire { p rivate R oom room ; / ge tte r and s e tte r m e th od s h e re /}p u b lic clas s Alarm {
  • 23 Definitionsru le "Wh e n th e re is a fire tu rn on th e s p rinkle r" wh e n F ire ($room : room ) $s p rinkle r : S p rinkle r( room = = $room , on = = fals e )th e n m od ify( $s p rinkle r ) { on = tru e }; p rintln( "Tu rn on th e s p rinkle r for room " + $room .nam e );e ndru le "Wh e n th e fire is gone tu rn off th e s p rinkle r" wh e n $room : R oom ( ) $s p rinkle r : S p rinkle r( room = = $room , on = = tru e ) not F ire ( room = = $room )th e n m od ify( $s p rinkle r ) { on = fals e }; p rintln( "Tu rn off th e s p rinkle r for room " + $room .nam e );
  • 24 Definitionsru le "R ais e th e alarm wh e n we h ave one or m ore fire s " wh e n e xis ts F ire ()th e n ins e rt( ne w Alarm () ); p rintln( "R ais e th e alarm " );e ndru le "C ance l th e alarm wh e n all th e fire s h ave gone " wh e n not F ire () $alarm : Alarm ()th e n re tract( $alarm ); p rintln( "C ance l th e alarm " );e nd
  • 25 Definitionsru le "S tatu s ou tp u t wh e n th ings are ok" wh e n not Alarm () not S p rinkle r( on = = tru e )th e n p rintln( "E ve ryth ing is ok" );e nd
  • 26 E xecutingS tring[] nam e s = ne w S tring[]{"kitch e n", "b e d room ", "office ", "livingroom "};M ap < S tring,R oom > nam e 2room = ne w H as h M ap < S tring,R oom > ();for( S tring nam e : nam e s ){ R oom room = ne w R oom ( nam e ); nam e 2room .p u t( nam e , room ); ks e s s ion.ins e rt( room ); S p rinkle r s p rinkle r = ne w S p rinkle r( room ); ks e s s ion.ins e rt( s p rinkle r );}ks e s s ion.fire AllR u le s ()> E ve ryth ing is ok
  • 27 E xecutingF ire kitch e nF ire = ne w F ire ( nam e 2room .ge t( "kitch e n" ) );F ire office F ire = ne w F ire ( nam e 2room .ge t( "office " ) );F actH and le kitch e nF ire H and le = ks e s s ion.ins e rt( kitch e nF ire );F actH and le office F ire H and le = ks e s s ion.ins e rt( office F ire );ks e s s ion.fire AllR u le s ();> R ais e th e alarm> Tu rn on th e s p rinkle r for room kitch e n> Tu rn on th e s p rinkle r for room office
  • 28 E xecutingks e s s ion.re tract( kitch e nF ire H and le );ks e s s ion.re tract( office F ire H and le );ks e s s ion.fire AllR u le s () > Tu rn off th e s p rinkle r for room office > Tu rn off th e s p rinkle r for room kitch e n > C ance l th e alarm > E ve ryth ing is ok ru le "S tatu s ou tp u t wh e n th ings are ok" wh e n not Alarm () not S p rinkle r( on = = tru e ) th e n p rintln( "E ve ryth ing is ok" );
  • 29 C onditional E lementsnot Bus( color = “red” )exists Bus( color = “red” )forall ( $bus : Bus( color == “red” ) )forall ( $bus : Bus( floors == 2 ) Bus( this == $bus, color == “red” ) )
  • 30A ccumulate C E ru le "accu m u late " wh e n $s u m : N u m b e r( intValu e > 1 00 ) from accu m u late ( Bu s ( color = = "re d ", $t : takings ) s u m ( $t ) ) th e n p rint "s u m is “ + $s u m ; e nd
  • 31Decis ion Table
  • 32 Decis ion Tablerule "Pricing bracket_10"when Driver(age >= 18, age <= 24, locationRiskProfile == "LOW", priorClaims == "1") policy: Policy(type == "COMPREHENSIVE")then policy.setBasePrice(450);
  • 33Types Types Layou t H orizontal Ve rtical Lim ite d e ntry E xte nd e d e ntry C ate goris ation E xp and e d form , contracte d form M u lti-h it, all h its M u lti-h it, firs t h it S ingle h it
  • 34Layouts Horizontal
  • 35Layouts Vertical
  • 36Layouts Limited entry
  • 37Layouts E xtended entry
  • 38C ategoris ation E xpanded form S ingle colu m n for e ve ry cond ition com b ination Th e nu m b e r of colu m ns s h ou ld e qu al th e p rod u ct of th e nu m b e r of s tate s for e ve ry cond ition. e .g. 2 cond itions , one with 3 s tate s th e oth e r 4 (s e e ab ove ): 3 * 4 = 1 2 com b inations e .g. 2 cond itions e ach with 3 s tate s and 1 cond ition with 4 s tate s give s : 3 * 3 * 4 = 36 com b inations
  • 39C ategoris ation E xpanded form (continued)
  • 40C ategoris ation C ontracted form C ontraction is th e firs t op tim is ation. R e d u ce s th e nu m b e r of cond ition colu m ns . R e m ove s im p os s ib le com b inations If th e s am e actions e xis t for ru le s cove ring all cond ition s tate s for a give n cond ition th e y can b e com b ine d and th e cond ition s tate b e com e s irre le vant.
  • 41C ategoris ation C ontracted form – s tage 1 R u le s 2 and 3 are im p os s ib le cond itions
  • 42C ategoris ation C ontracted form – s tage 2 M e rge ad j nt colu m n grou p s with id e ntical action p arts ace
  • 43C ategoris ation Multi-hit, all hits To ge t com p le te re s u lt all ru le s m atch ing ne e d to h ave th e ir actions e xe cu te d . C ond ition colu m ns are not m u tu ally e xclu s ive If th e C ond ition colu m ns are not e xclu s ive , s om e com b ination of cond itions are p re s e nt in m ore th an one colu m n, wh ich m ay le ad to am b igu ity or incons is te ncy.
  • 44C ategoris ation Multi-hit, all hits G ive n a 35 ye ar old with 1 5 ye ars s e rvice 1 . R u le 1 m atch e s , giving 22 d ays 2. R u le 5 m atch e s , giving an ad d itional 3 d ays 3. A total of 25 d ays is as s igne d
  • 45C ategoris ation Multi-hit, firs t hit To ge t com p le te re s u lt th e firs t ru le (from le ft-to-righ t) m atch ing ne e d s to h ave its action e xe cu te d . C ond ition colu m ns are not m u tu ally e xclu s ive . If th e C ond ition colu m ns are not e xclu s ive , s om e com b ination of cond itions are p re s e nt in m ore th an one colu m n, wh ich m ay le ad to am b igu ity or incons is te ncy.
  • 46C ategoris ation Multi-hit, firs t hit G ive n a 35 ye ar old with 1 5 ye ars s e rvice 1 . R u le 4 m atch e s , giving 25 d ays 2. R u le 6 wou ld m atch b u t R u le 4 was th e firs t h it
  • 47C ategoris ation S ingle hit E ach p os s ib le com b ination of cond itions m atch e s e xactly one , and only one , ru le . C ond ition colu m ns are m u tu ally e xclu s ive As th e C ond ition colu m ns are e xclu s ive ; com b inations of cond itions cannot b e p re s e nt in m ore th an one colu m n wh ich e lim inate s am b igu ity and incons is te ncy. C las s ic form if S ingle -h it is "e xp and e d d e cis ion tab le "; b u t th is can b e op tim is e d or “contracte d ”.
  • 48C ategoris ation S ingle hit G ive n a 35 ye ar old with 1 5 ye ars s e rvice 1 . R u le 3 m atch e s , giving 25 d ays 2. N o oth e r ru le s m atch
  • 49Validation & Verification Redundancy - S ubs umption
  • 50Validation & Verification Deficiency P re m iu m is £500 if ap p licant age is le s s th an 30 P re m iu m is £300 if Ye ars With ou t C laim is gre ate r th an or e qu al to 1 0 ye ars . Ap p licant is 29, p re m iu m is £500 Ap p licant h as 1 2 ye ars with ou t claim , p re m iu m is £300 Ap p licant is 29 with 1 2 ye ars with ou t claim , p re m iu m is ?!?
  • 51Decis ion Tables in G uvnor Vid e o d e m ons tration
  • 52Guided Editor
  • 53Decision Table
  • 54Decision Table
  • 55Decision Table
  • 56Decision Tables – Cell merging.. etc Cell Merging Cell Grouping Typed Columns Sorting by column Negate pattern Support for Ohterwise
  • 57Decision Tables – Wizard
  • 58Rule Templates
  • 59Rule Templates
  • 60Rule Templates
  • 61Scenario Testing
  • 62TMS and Inference ru le "Is s u e C h ild Bu s P as s " Couples the logic wh e n $p : P e rs on( age < 1 6 ) th e n ins e rt(ne w C h ild Bu s P as s ( $p ) ); e nd ru le "Is s u e Ad u lt Bu s P as s " What happens when the Child stops being 16? wh e n $p : P e rs on( age > = 1 6 ) th e n ins e rt(ne w Ad u ltBu s P as s ( $p ) ); e nd
  • 63TMS and Inference Bad Monolith ic Le aky Brittle inte grity - m anu al m ainte nance
  • 64TMS and Inference A ru le “logically” ins e rts an ob j ct e Wh e n th e ru le is no longe r tru e , th e ob j ct is re tracte d . e wh e n de-couples the logic $p : P e rs on( age < 1 6 ) th e n logicalIns e rt( ne w Is C h ild ( $p ) ) e nd wh e n Maintains the truth by automatically retracting $p : P e rs on( age > = 1 6 ) th e n logicalIns e rt( ne w Is Ad u lt( $p ) ) e nd
  • 65TMS and Inference ru le "Is s u e C h ild Bu s P as s " wh e n $p : P e rs on( ) Is C h ild ( p e rs on = $p ) th e n logicalIns e rt(ne w C h ild Bu s P as s ( $p ) ); e nd The truth maintenance ru le "Is s u e Ad u lt Bu s P as s " cascades wh e n $p : P e rs on( age > = 1 6 ) Is Ad u lt( p e rs on = $p ) th e n logicalIns e rt(ne w Ad u ltBu s P as s ( $p ) ); e nd
  • 66TMS and Inference ru le "Is s u e C h ild Bu s P as s " wh e n $p : P e rs on( ) not( C h ild Bu s P as s ( p e rs on = = $p ) ) th e n re qu e s tC h ild Bu s P as s ( $p ); The truth maintenance cascades e nd
  • 67TMS and Inference G ood D e -cou p le knowle d ge re s p ons ib ilitie s E ncap s u late knowle d ge P rovid e s e m antic ab s tractions for th os e e ncap s u lation Inte grity rob u s tne s s – tru th m ainte nance
  • 68 Ques tions ? D ave Bowm an: All righ t, H AL; Ill go in th rou gh th e e m e rge ncy airlock. H AL: With ou t you r s p ace h e lm e t, D ave , you re going to find th at rath e r d ifficu lt. D ave Bowm an: H AL, I wont argu e with you anym ore ! O p e n th e d oors ! H AL: D ave , th is conve rs ation can s e rve no p u rp os e anym ore . G ood b ye .essor Falken.oshua. . The only winning move is not to play. How about a nice game of chess