rules, events and workflow

Mark Proctor Project Lead T he 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 29th

In a panic, they try to pull the plug.

Boot Camps San Francisco 2009 (40+ attendees) Sponsored by Third Pillar

Sun, FAMC, OSDE, Kaseya, Fedex, TU Group, Intermountain Healthcare, Gap, Sony Pictures, Lockheed Martin, Kaiser, HP, Wells Fargo, US Navy Research, FOLIOfn, Boeing ..... San Diego 2010 (80+ attendess) Sponsored by US Navy

5 day event, with 2 days focus on the healthcare industry

OSDE, AT&T, SAIC, US Navy Research, Kaiser, Clinica, Intermountain Healthcare, GE Healthcare, VA, Boeing, Nationwide ....

Books Introduction to Expert Systems Peter Jackson Expert Systems, Principles and Programming Joseph C. Giarratano and Gary D. Riley

Oh And There are Drools Books Too

It All Started Here Birth of CDSS Clinical Decision Support Systems 1970s 1980s Dendral Baobab Mycin Guidon Neomycin Teiresias Puff Emycin WM Sacon Centaur Wheeze Gravida Clot Oncocin

Because Not Everyone Is As Smart As He Is

Business Rules Engines 1980s 2010s 1990s 2000s OPS5 ART Clips Jess Drools 2 JRules Drools 3 Drools 4 Drools 5

Drools History Drools 2 Rete like XML Scripting language Drools 3 Based on Clips functionality

Iterative improves to JRules syntax with Clips functionality Drools 4 More declarative

Basic functional programming feature with “from”

Basic BRMS Drools 5 Improved functional programming with 'accumulate'

More Advanced Rule Flow integration

Complex Event Process Temporal Comparators

Sliding Time Windows Production ready BRMS (Guvnor)

Drools History Drools 5.1 Differential Diff (true modify) Drools 5.2 Freeform expressions between patterns

Prolog like derivation queries

Decision tables and rule templates (Guvnor)

Pure GWT (Guvnor) Drools 5.3 ...

Integrated Systems Semantic Ontologies Rules Event Processes Workflows Rules Workflows Event Processes Semantic Ontologies

Rules and processes loosely coupled tightly coupled specific generic Decision Services Process Rules SCOPE COUPLING ?

Declarative Programming Production Rule Systems PRD (forward chaining) Reactive

when Alarm( status == “alert” ) then send( “warning” ) Logic Programming LP (backward chaining) Query

descendant( “mary”, “jane”) Functional Programming FP Map,Fold, Filter

avg([12, 16, 4, 6]) Returns single value 9.5 round([10.3, 4.7, 7.8] ) Returns List [10, 5, 8] Description Logic Person Has Name and LivesAt Address

rule “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 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.amount Credit Cashflow Rule

rule “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 rule “decrease balance for AccountPeriod Debits” when ap : AccountPeriod() acc : Account( $accountNo : accountNo ) CashFlow( type == DEBIT, accountNo == $accountNo, date >= ap.start && <= ap.end, $ammount : ammount ) then acc.balance -= $amount; end Rules as a “view”

Definitions public class Room { private String name // getter and setter methods here } public class Sprinkler { private Room room ; private boolean on ; // getter and setter methods here } public class Fire { private Room room ; // getter and setter methods here } public class Alarm { }

Definitions rule "When there is a fire turn on the sprinkler" when Fire ($room : room) $sprinkler : Sprinkler ( room == $room, on == false ) then modify ( $sprinkler ) { on = true }; println ( "Turn on the sprinkler for room " + $room.name ); end rule "When the fire is gone turn off the sprinkler" when $room : Room ( ) $sprinkler : Sprinkler ( room == $room, on == true ) not Fire ( room == $room ) then modify ( $sprinkler ) { on = false }; println ( "Turn off the sprinkler for room " + $room.name ); end

Definitions rule "Raise the alarm when we have one or more fires" when exists Fire () then insert ( new Alarm () ); println ( "Raise the alarm" ); end rule "Cancel the alarm when all the fires have gone" when not Fire () $alarm : Alarm () then retract ( $alarm ); println ( "Cancel the alarm" ); end

Definitions rule "Status output when things are ok" when not Alarm () not Sprinkler ( on == true ) then println ( "Everything is ok" ); end

Executing String [] names = new String []{ "kitchen" , "bedroom" , "office" , "livingroom" }; Map < String , Room > name2room = new HashMap < String , Room >(); for ( String name : names ){ Room room = new Room ( name ); name2room .put( name , room ); ksession .insert( room ); Sprinkler sprinkler = new Sprinkler ( room ); ksession .insert( sprinkler ); } ksession .fireAllRules() > Everything is ok

Executing Fire kitchenFire = new Fire ( name2room.get( "kitchen" ) ); Fire officeFire = new Fire ( name2room.get( "office" ) ); FactHandle kitchenFireHandle = ksession .insert( kitchenFire ); FactHandle officeFireHandle = ksession .insert( officeFire ); ksession .fireAllRules(); > Raise the alarm > Turn on the sprinkler for room kitchen > Turn on the sprinkler for room office

Executing ksession .retract( kitchenFireHandle ); ksession .retract( officeFireHandle ); ksession .fireAllRules() > Turn off the sprinkler for room office > Turn off the sprinkler for room kitchen > Cancel the alarm > Everything is ok rule "Status output when things are ok" when not Alarm () not Sprinkler ( on == true ) then println ( "Everything is ok" ); end

not Bus( color = “red” ) Conditional Elements exists Bus( color = “red” ) forall ( $bus : Bus( floors == 2 ) Bus( this == $bus, color == “red” ) ) forall ( $bus : Bus( color == “red” ) )

Accumulate CE rule "accumulate" when $sum : Number( intValue > 100 ) from accumulate ( Bus( color == "red" , $t : takings ) sum( $t ) ) then print "sum is “ + $sum; end

Accumulate CE Patterns and CE's can be chained with ' from ' rule "collect" when $zipCode : ZipCode() $sum : Number( intValue > 100 ) from accumulate ( Bus( color == "red" , $t : takings ) from $hbn.getNamedQuery( “Find Buses” ) .setParameters( [ “zipCode” : $zipCode ] ) .list(), sum( $t ) ) then print "sum is “ + $sum; end

Timers rule “name” timer ( cron: 0 0/15 * * * * ) when Alarm( ) then sendEmail( ”Alert Alert Alert!!!” ) Field Name Mandatory? Allowed Values Allowed Special Characters Seconds YES 0-59 , - * / Minutes YES 0-59 , - * / Hours YES 0-23 , - * / Day of month YES 1-31 , - * ? / L W Month YES 1-12 or JAN-DEC , - * / Day of week YES 1-7 or SUN-SAT , - * ? / L # Year NO empty, 1970-2099 , - * / Send alert every quarter of an hour

Calendars rule "weekdays are high priority" calendars "weekday" timer (int:0 1h) when Alarm() then send( "priority high - we have an alarm” ); end rule "weekend are low priority" calendars "weekend" timer (int:0 4h) when Alarm() then send( "priority low - we have an alarm” ); end Execute now and after 1 hour duration Execute now and after 4 hour duration

Backward Chaining query isContainedIn( String x, String y ) Location ( x, y; ) or ( Location ( z , y; ) and ?isContainedIn ( x, z ; ) ) end rule reactiveLook when Here ( place : place) ?isContainedIn ( place, "keys" ; ) then System.out.println( "We have found your keys" ); end

Drools Fusion: Enables... Event Detection: From an event cloud or set of streams, select all the meaningful events, and only them. [Temporal] Event Correlation: Ability to correlate events and facts declaring both temporal and non-temporal constraints between them.

Ability to reason over event aggregation. Event Sequencing: A -> ( B OR C ) -> D Event Abstraction: Ability to compose complex events from atomic events AND reason over them

Drools Fusion: Features Support to Event semantics: Usually immutable, but not enforced

Point-in-time and Interval events Time semantics Discrete Temporal relationships All 13 operators defined by James Allen (+ negations)

Event Driven Architecture (EDA) “ Event Driven Architecture (EDA) is a software architecture pattern promoting the production , detection , consumption of, and reaction to events. An event can be defined as "a significant change in state"[1]. For example, when a consumer purchases a car, the car's state changes from "for sale" to "sold". A car dealer's system architecture may treat this state change as an event to be produced, published, detected and consumed by various applications within the architecture.”

CEP vs EDA vs SOA CEP is component of EDA

Metaphor In our body : SOA is used to build our muscles and organs

EDA is used to build our sensory system

$c : Custumer( type == “VIP ) BuyOrderEvent( customer == $c ) from entry-point “Home Broker Stream” Scalability EntryPoint entryPoint = session. getEntryPoint ( “Home Broker Stream” ); entryPoint.insert( event ) ; So lets allow multiple named entry points for those streams So now we can insert different streams concurrently Patterns can now optional specify their entry-point. When not specified uses the “default” entry-point

declare StockTick @role( event ) end declare StockTick @role( event ) @timestamp( timestampAttr ) companySymbol : String stockPrice : double timestampAttr : long end Automatic Life-Cycle Management Just use the declare statement to declare a type as an event and it will be retracted when it is no longer needed The declare statement can also specify an internal model, that external objects/xml/csv map on to. We support Smooks and JAXB

$c : Custumer( type == “VIP ) $oe : BuyOrderEvent( customer == $c ) from entry-point “Home Broker Stream” BuyAckEvent( relatedEvent == $oe.id, this after[1s, 10s] $oe ) from entry-point “Stock Trader Stream” coincides

finishedby Operators The Full set of Operators are supported BackAckEvent must occur between 1s and 10s ' after' BuyOrderEvent

Drools Fusion: Temporal Reasoning

$c : Custumer( type == “VIP ) $oe : BuyOrderEvent( customer == $c ) from entry-point “Home Broker Stream” not BuyAckEvent( relatedEvent == $oe.id, this after[1s, 10s] $oe ) from entry-point “Stock Trader Stream” Operators Existing Drools 'not' Conditional Elements can be used to detect non-occurrence of events BackAckEvent must occur between 1s and 10s ' after' BuyOrderEvent

Aggregations $n : Number( intValue > 100 ) from accumulate ( $s : StockTicker( symbol == “RHAT” ) over window:time ( 5s ), average ( $s.price ) ) Over 5 seconds Aggregate ticker price for RHAT over last 5 seconds The pattern 'Number' reasons 'from' the accumulate result

Aggregations acc ( $s : StockTicker( symbol == “RHAT” ) over window:time ( 5s ); $min : min ( $s.price ), $min : min ( $s.price ), $avg : avg ( $s.price ); $min > 10 && $max < 20 && $avg > 16 ) Over 5 seconds functions Accumlate over data Guard constraint

Aggregations Rule Engines do not deal with aggregations $n : Number( intValue > 100 ) from accumulate ( $s : StockTicker( symbol == “RHAT” ) over window:time ( 5s ), average ( $s.price ) ) Over 5 seconds Aggregate ticker price for RHAT over last 5 seconds The pattern 'Number' reasons 'from' the accumulate result

CEP Applied at FedEx Custom Critical Time specific deliveries for critical freight

Exclusive use non-stop door-to-door services

Blended Surface and Air services to minimize cost and transit time

Extra care in handling and specially equipped vehicles Temperature Control, Secured Services, Hazardous Material, Constant Surveillance * Presented by Adam Mollemkopf at ORF 2009

CEP Applied at FedEx Custom Critical * Presented by Adam Mollemkopf at ORF 2009

CEP Applied at FedEx Custom Critical * Presented by Adam Mollemkopf at ORF 2009 At least 50% of Alerts can be reasoned automatically, promoting staff savings and improved Customer and Driver experiences.

Risk Avoidance via pro-active monitoring Reduction in insurance claims and shipment service failures Minimum 30% efficiency gains in shipment monitoring , saving at least 15% of Operations staff cost.

CEP Applied at FedEx Custom Critical Some numbers (from early 2010): 24 x 7 sessions, no downtime

Average of 500k+ facts/events concurrently in memory Business hours: 1M+ facts/events concurrently Response time for reasoning cycles: Average: 150 ms

Peak: 1.2 sec Several hundred rules

TMS and Inference rule "Issue Child Bus Pass" when $p : Person ( age < 16 ) then insert(new ChildBusPass ( $p ) ); end rule "Issue Adult Bus Pass" when $p : Person ( age >= 16 ) then insert(new AdultBusPass ( $p ) ); end Couples the logic What happens when the Child stops being 16?

TMS and Inference Bad Monolithic

Brittle integrity - manual maintenance

TMS and Inference A rule “logically” inserts an object

When the rule is no longer true, the object is retracted. when $p : Person ( age < 16 ) then logicalInsert ( new IsChild ( $p ) ) end when $p : Person ( age >= 16 ) then logicalInsert ( new IsAdult ( $p ) ) end de-couples the logic Maintains the truth by automatically retracting

TMS and Inference rule "Issue Child Bus Pass" when $p : Person ( ) IsChild ( person == $p ) then logicalInsert ( new ChildBusPass ( $p ) ); end rule "Issue Adult Bus Pass" when $p : Person ( age >= 16 ) IsAdult ( person == $p ) then logicalInsert ( new AdultBusPass ( $p ) ); end The truth maintenance cascades

TMS and Inference rule "Issue Child Bus Pass" when $p : Person ( ) not ( ChildBusPass ( person == $p ) ) then requestChildBusPass( $p ); end The truth maintenance cascades

TMS and Inference Good De-couple knowledge responsibilities

Provide semantic abstractions for those encapsulation

Integrity robustness – truth maintenance

Some decisions are complex What insurance premium should I charge?

rules, events and workflow

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rules, events and workflow