Drools New York City workshop 2011

1. Mark Proctor Project Lead T he SkyNet funding bill is passed.

2. The system goes online on August 4th, 1997.

3. Human decisions are removed from strategic defense.

4. SkyNet begins to learn at a geometric rate.

5. It becomes self-aware at 2:14am Eastern time, August 29th

6. In a panic, they try to pull the plug.

7. And, Skynet fights back

8. Drools Vs BRMS Repository Rules and Metadata JBoss Enterprise BRMS Platform JBoss Rules Rules Engine/ Execution Rules Development & Management CEP Complex Events Jboss EWP

9. Drools Vs BRMS Stand-alone Framework

10. Innovation

11. Fast and Frequent Releases

12. Tested on developer environments

13. Suitable for development and prototype

14. No Patches – On to next big thing

15. Non SLA bound forum support Fully Integrated Platform

16. Same Innovated Code + Stability

17. Stability – Includes usability, performance and security issues identified during productization

18. Certified for Multi-OS, Multi-DB

19. Best for Development and Production

20. 5 Yr lifecycle – Includes patches, updates, backward compatibility

21. SLA based full support from dedicated support engineers

22. Open Source Assurance – legal protection derived

23. JBoss Community & Enterprise Changes Community / Enterprise Platform Delta BRMS Drools Enterprise Platforms - Performance, Scalability, Security and Qualityimprove over time without compromising long-term compatibility. 08/09 12/10 03/11 05/11 Over 150+ defect fixes & 35+ OS/JVM/DB certifications (& counting) between Drools Community 5.1 & JBoss BRMS 5.1.x

24. Evolution of JBoss BRMS 05/08 04/09 12/10 ? Lightweight Business Rules Engine Business Rules Engine

25. Authoring and Management App

26. Repository

27. EAP Certified

28. DBs, JVMs Business Rules Engine

29. Authoring and Management App

30. Choice of Repository

31. Integrated Container

32. CEP – Tech Preview

33. EAP, EWS, EWP, JVMs, Dbs and more

34. Performance Repository JBoss Enterprise BRMS Platform 5.0 JBoss Rules BRM Repository JBoss Enterprise BRMS Platform 5.1 JBoss Rules BRM CEP Jboss EWP

35. Back Up

37. Agenda Community

38. History

39. Declarative Programming

40. Drools Expert Quick Example Stateless/Stateful

41. Conditional elements

42. Timers and Calendars

43. Truth maintenance and Inference

44. Backward chaining Decision Tables Decisions and Decision Tables

45. Types

46. Validation and Verification

47. Transformation

48. Decision tables in guvnor Roadmap

49. Sample Industries and Users Investment Millennium Investment Group (MIG) Logistics Fedex Airline Sabre Mortgage Franklin American Healthcare OSDE

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

51. 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

52. 5 day event, with 2 days focus on the healthcare industry

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

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

55. Oh And There are Drools Books Too

57. History

64. Types

66. Transformation

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

69. Because Not Everyone Is As Smart As He Is

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

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

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

73. Basic functional programming feature with “from”

74. Basic Rule Flow

75. Basic BRMS Drools 5 Improved functional programming with 'accumulate'

76. More Advanced Rule Flow integration

77. Complex Event Process Temporal Comparators

78. Sliding Time Windows Production ready BRMS (Guvnor)

79. Drools History Drools 5.1 Differential Diff (true modify) Drools 5.2 (this month) Freeform expressions between patterns

80. Multi-function accumulates

81. Prolog like derivation queries

82. Decision tables and rule templates (Guvnor)

83. Pure GWT (Guvnor)

85. History

92. Types

94. Transformation

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

97. Integrated Systems

98. Declarative Programming Production Rule Systems PRD (forward chaining) Reactive

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

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

101. 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

102. Concepts Overview

106. History

113. Types

115. Transformation

117. Classes

118. 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

119. 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”

121. History

128. Types

130. Transformation

132. Definitions public class Applicant { private String name; private int age; private boolean valid; // getter and setter methods here } rule "Is of valid age" when $a : Applicant( age < 18 ) then modify ( $a ) { valid = false }; ends

133. Building KnowledgeBuilder kbuilder = KnowledgeBuilderFactory .newKnowledgeBuilder(); kbuilder .add( ResourceFactory .newClassPathResource( "licenseApplication.drl" , getClass() ), ResourceType.DRL ); if ( kbuilder .hasErrors() ) { System.err.println( kbuilder .getErrors().toString() ); } kbase .addKnowledgePackages( kbuilder .getKnowledgePackages() );

134. Executing StatelessKnowledgeSession ksession = kbase .newStatelessKnowledgeSession(); Applicant applicant = new Applicant ( "Mr John Smith" , 16 ); assertTrue ( applicant .isValid() ); ksession .execute( applicant ); assertFalse ( applicant .isValid() ); rule "Is of valid age" when $a : Applicant( age < 18 ) then modify ( $a ) { valid = false }; ends

135. Agenda Product Overview

136. Community

137. History

144. Types

146. Transformation

148. 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 { }

149. 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

150. 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

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

152. 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

153. 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

154. 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

156. History

163. Types

165. Transformation

167. 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” ) )

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

169. 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

170. 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

171. $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

173. History

180. Types

182. Transformation

184. 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

185. 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

187. History

194. Types

196. Transformation

198. 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?

199. TMS and Inference Bad Monolithic

200. Leaky

201. Brittle integrity - manual maintenance

202. TMS and Inference A rule “logically” inserts an object

203. 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

204. 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

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

206. TMS and Inference Good De-couple knowledge responsibilities

207. Encapsulate knowledge

208. Provide semantic abstractions for those encapsulation

209. Integrity robustness – truth maintenance

211. History

218. Types

220. Transformation

222. Backward Chaining query isChild( Person p ) $p : Person ( age <= 16 ) end rule "Issue Child Bus Pass" when $p : Person ( ) ?isChild ( $p ) then logicalInsert ( new ChildBusPass ( $p ) ); end

223. 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

225. History

232. Types

234. Transformation

236. Some decisions are simple I want to treat myself

237. Some decisions are simple

238. Some decisions are complex What insurance premium should I charge?

239. Some decisions are complex Business considerations Applicant's age

240. Applicant's experience

241. Sailing qualifications

242. Value of vessel

243. Type of cover

244. Some decisions are complex Business considerations Applicant's age Only insure people over 25 years of age

245. If less than 35 add 10% surcharge

246. If less than 45 add 5% surcharge

247. Some decisions are complex Business considerations Applicant's experience If sailing < 5 years then charge +10% Sailing qualifications None charge +100% Etc...

248. Enter the decision table Extract of decision table

249. Decision Table

250. Decision Table rule "Pricing bracket_10" when Driver(age >= 18, age <= 24, locationRiskProfile == "LOW", priorClaims == "1") policy: Policy(type == "COMPREHENSIVE") then policy.setBasePrice(450); end

251. Decision Tables in Guvnor Generation of DRL

252. Each row compiles into a separate DRL rule

253. Decision Tables in Guvnor Generation of DRL

254. Each row compiles into a separate DRL rule

255. rule "Row 1 dtable"

256. dialect "mvel"

257. when

258. $person : Person( age < "18" )

259. then

260. $person.setHolidayEntitlement( 27 );

261. end

262. rule "Row 2 dtable"

263. dialect "mvel"

264. when

265. $person : Person( age >= "18" , age < "45" , lengthOfService < "15" )

266. then

267. $person.setHolidayEntitlement( 22 );

268. end

270. History

277. Types

279. Transformation

281. Types Types Layout Horizontal

282. Vertical

283. Limited entry

284. Extended entry Categorisation Expanded form, contracted form

285. Multi-hit, all hits

286. Multi-hit, first hit

287. Single hit

288. Layouts Horizontal

289. Layouts Vertical

290. Layouts Limited entry

291. Layouts Extended entry

292. Categorisation Expanded form Single column for every condition combination

293. The number of columns should equal the product of the number of states for every condition. e.g. 2 conditions, one with 3 states the other 4 (see above): 3 * 4 = 12 combinations e.g. 2 conditions each with 3 states and 1 condition with 4 states gives: 3 * 3 * 4 = 36 combinations

294. Categorisation Expanded form (continued) Condition states should be digitised e.g. age: <18, 18-35, 36-60, >60 not a free-form integer field Condition states should be continuous e.g. GOOD: age<18, age>=18; BAD: age<18, age>=30

295. Not essential for expansion but ensures completeness This guarantee of completeness of condition combinations is one of the main advantages of decision tables.

296. Categorisation Expanded form (continued)

297. Categorisation Contracted form Contraction is the first optimisation.

298. Reduces the number of condition columns.

299. Removes impossible combinations

300. If the same actions exist for rules covering all condition states for a given condition they can be combined and the condition state becomes irrelevant.

301. Categorisation Contracted form – stage 1 Rules 2 and 3 are impossible conditions

302. Categorisation Contracted form – stage 2 Merge adjacent column groups with identical action parts

303. Categorisation Multi-hit, all hits To get complete result all rules matching need to have their actions executed.

304. Condition columns are not mutually exclusive

305. If the Condition columns are not exclusive, some combination of conditions are present in more than one column, which may lead to ambiguity or inconsistency.

306. Categorisation Multi-hit, all hits Given a 35 year old with 15 years service 1. Rule 1 matches, giving 22 days 2. Rule 5 matches, giving an additional 3 days 3. A total of 25 days is assigned

307. Categorisation Multi-hit, first hit To get complete result the first rule (from left-to-right) matching needs to have its action executed.

308. Condition columns are not mutually exclusive.

309. If the Condition columns are not exclusive, some combination of conditions are present in more than one column, which may lead to ambiguity or inconsistency.

310. Categorisation Multi-hit, first hit Given a 35 year old with 15 years service 1. Rule 4 matches, giving 25 days 2. Rule 6 would match but Rule 4 was the first hit

311. Categorisation Single hit Each possible combination of conditions matches exactly one, and only one, rule.

312. Condition columns are mutually exclusive

313. As the Condition columns are exclusive; combinations of conditions cannot be present in more than one column which eliminates ambiguity and inconsistency.

314. Classic form if Single-hit is "expanded decision table"; but this can be optimised or “contracted”.

315. Categorisation Single hit Given a 35 year old with 15 years service 1. Rule 3 matches, giving 25 days 2. No other rules match

317. History

324. Types

326. Transformation

328. Validation & Verification Redundancy

329. Ambivalence (conflicting)

330. Deficiency

331. Mitigation

332. Validation & Verification Redundancy Duplication

333. Subsumption Two rules have the same actions, but one contains additional conditions in which it will succeed. Whenever the more restrictive rule succeeds, the less restrictive rule also succeeds, resulting in redundancy. Impossible conditions Conditions that do not make sense (e.g. age < 0)

334. Validation & Verification Redundancy - Subsumption

335. Validation & Verification Ambivalence Contradictory rules Two (or more) rules have the same conditions but with different actions. Conflicting rules An action for one rule is the opposite of the same action for the other rule. Ambiguous rules The actions are different but not opposite.

336. Validation & Verification Deficiency Missing rules A situation exists in which a particular action is required, but there is no rule that succeeds in that situation and produces the desired conclusion. Unused input state or domain attribute

337. Missing knowledge The absence of some essential elements from the problem situation.

338. Validation & Verification Deficiency Premium is £500 if applicant age is less than 30

339. Premium is £300 if Years Without Claim is greater than or equal to 10 years. Applicant is 29, premium is £500

340. Applicant has 12 years without claim, premium is £300 Applicant is 29 with 12 years without claim, premium is ?!?

342. History

349. Types

351. Transformation

353. Transformation Decision Trees Requirements Digitised condition states

354. Each Condition in a column becomes a branch Multi-hit Walk every path through tree Single-hit Walk single path through tree

355. Transformation Example - Decision Table

356. Transformation Example - Decision Tree

357. Transformation Paying attention?

358. Rules 1, 2 and 3, 4 have the same action no matter what the state of “Hunger”. Therefore they can be contracted...

360. History

367. Types

369. Transformation

371. Decision Tables in Guvnor What do we currently provide? Vertical layout

372. Generalised categorisation Multi-hit, all hit; Multi-hit first hit; Single hit Extended entry

373. Digitised condition states (enumerated fields)

374. New decision table editor Foundation for future work

375. Condition and rule negation

376. Cell merging

377. Cell grouping

378. Decision Tables in Guvnor What do we currently provide? Cell merging

379. Decision Tables in Guvnor What do we currently provide? Cell grouping

380. Decision Tables in Guvnor What do we currently provide? Video demonstration Demonstration available online at http://vimeo.com/21438537

382. History

389. Types

391. Transformation

393. Roadmap Decision Tables Provision of other layouts Horizontal

394. Limited entry Automated optimisation

395. Improved support for digitised condition states

396. Integration of V&V providing visual feedback Pluggable editors for Model Fact Types

397. Usability enhancements

398. Roadmap Drools Improved backward chaining (open queries)

399. Optimistic backward chaining.

400. Business Semantics (ontologies)

401. Simulation Testing

402. High Availability

403. Distributed (Grid) platform

404. Runtime Management

405. BAM

406. Questions? Questions? Dave Bowman : All right, HAL; I'll go in through the emergency airlock.

407. HAL : Without your space helmet, Dave, you're going to find that rather difficult.

408. Dave Bowman : HAL, I won't argue with you anymore! Open the doors!

409. HAL : Dave, this conversation can serve no purpose anymore. Goodbye. Joshua: Greetings, Professor Falken. Stephen Falken : Hello, Joshua. Joshua: A strange game. The only winning move is not to play. How about a nice game of chess?

Drools New York City workshop 2011

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Drools New York City workshop 2011

Editor's Notes