Reaction RuleML 1.0 Tutorial - Standardized Semantic Reaction Rules

1. Tutorial:Deliberation RuleML, Reaction RuleML, and LegalRuleML: Specification and Application Harold Boley, Adrian Paschke, Tara Athan, Monica Palmirani, and Guido Governatori Part 2 - Reaction RuleMLAdrian Paschke, Harold Boley and Tara Athan (RuleML Initiative) The 6th International Symposium on Rules: Research Based and Industry Focused (RuleML-2012) Aug. 27-29, 2012, Montpellier, France 0

2. The RuleML Family Reaction Harold Boley, Adrian Paschke and Omair Shafiq: RuleML 1.0: The Overarching Specification of Web Rules , in Semantic Web Rules, Proceedings of RuleML-2010, Lecture Notes in Computer Science, 2010, Volume 6403/2010, 162-178. 1

3. Reaction Rules: Four Sub-branches• PR RuleML: Production Rules (Condition-Action rules)• ECA RuleML: Event-Condition-Action (ECA) rules• CEP RuleML: Rule-based Complex Event Processing (complex event processing reaction rules, (distributed) event messaging reaction rules, query reaction rules, etc.)• DR and KR RuleML: Knowledge Representation AI Reasoning withTemporal/Event/Action/Situation/ Transition/Process Logics and Calculi Adrian Paschke, Harold Boley, Zhili Zhao, Kia Teymourian and Tara Athan. Reaction RuleML 1.0: Standardized Semantic Reaction Rules, 6th International Conference on Rules (RuleML 2012), Montpellier, France, August 27-31, 2012 2

4. Reaction Rules: Specializable Syntax <Rule @key @keyref @style>Info, Life <meta> <!-– descriptive metadata of the rule --> </meta>Cycle Mgt. <scope> <!–- scope of the rule e.g. a rule module --> </scope> <evaluation>  </evaluation> Interface <signature> <!– rule signature --> </signature> <qualification> <!–- e.g. qualifying rule metadata, e.g. priorities, validity, strategy --> </qualification> <quantification>  </quantification> <oid> <!– object identifier --> </oid> <on> <!–- event part --> </on> <if> <!–- condition part --> </if> Imple- <then> <!-– (logical) conclusion part --> </then> mentation <do>  </do> <after> <!–- postcondition part after action, e.g. to check effects of execution --> </after> <else> <!–- (logical) else conclusion --> </else> <elsedo>  </elsedo> </Rule> 3

5. Reaction RuleML – Example Rule Types• Derivation Rule: <Rule style="reasoning"> (temporal/event/action/situation <if>...</if> reasoning) <then>...</then> </Rule>• Production Rule: <Rule style="active"> <if>...</if> <do>...</do> </Rule>• Trigger Rule: <Rule style="active"> > <on>...</on> <do>...</do> </Rule>• ECA Rule: <Rule style="active"> <on>...</on> <if>...</if> <do>...</do> </Rule> 4

6. Rule Interface and Rule Implementation<Rule style="active"> <evaluation> <Profile type="&ruleml;DefiniteProductionRule" direction="forward" style="active"/> </evaluation> <signature> Interface with <Atom> evaluation <op><Rel>likes</Rel></op> <arg><Var mode="+"/></arg>  semantics and <arg><Var mode="?"/> </arg> rule signature </Atom> declaration</signature><if> <Atom> <op><Rel>likes</Rel></op> <arg><Var>X</Var></arg> <arg><Ind>wine</Ind></arg> </Atom> </if> <do> Implementation <Assert> <formula> <Atom> <op><Rel>likes</Rel></op> <arg><Ind>John</Ind></arg> <arg><Var>X</Var></arg> </Atom> </formula> </Assert> </do>Paschke, A., Boley, H., Kozlenkov. A., Craig, B.: Rule Responder: RuleML-Based Agents for Distributed Collaboration 5</Rule> on the Pragmatic Web, 2nd International Conference on the Pragmatic Web, Tilburg, Netherlands, 2007.

7. Separation of Interface and Implementation (Distributed Knowledge) <Rule key="ruleinterface1"> <evaluation><Profile> ...p1… </Profile><evaluation> <evaluation><Profile> …p2 …</Profile></evaluation> Interface 1 (@key=ruleinterface1) <signature>…s1…</signature> </Rule> <Rule key="ruleinterface2"> <evaluation><Profile> ...p3… </Profile><evaluation> Interface 2 <signature>…s2…</signature> (@key=ruleinterface2) </Rule> … <Rule keyref=“ruleinterface2" key=" ruleimpl2 "> <if> … </if> Implementation 2 <do> </do> (@keyref=ruleinterface2) </Rule> <Rule keyref="rulreinterface1" key="ruleimpl1 "> <if> … </if> <do> </do> Implementation 1 (@keyref=ruleinterface1) </Rule>A. Paschke, H. Boley, P. Vincent, D. Sottara, T. Athan: Reaction RuleML for Loosley-Coupled and Event-Messaged KBs, OMG API4KB 6Technical Meeting, Ontology PSIG, Cambridge, MA, 21 June 2012, http://www.cs.unb.ca/~boley/talks/ReactionRuleML-OMG.pdf

8. Example: Distributed Rule Interface and Implementation<-- rule interface with two alternative interpretation semantics and a signature. The interface references the implementation identified by the corresponding key --><Rule keyref=“#r1"> Interface<evaluation index="1">  with <Profile type="&ruleml;Well-Founded-Semantics" direction="backward"/> evaluation</evaluation> semantics<evaluation index="2"> and  <Profile type="&ruleml;Answer-Set-Semantics" direction="backward"/> public rule</evaluation> signature<signature> <Atom><Rel>getPapers</Rel><Var mode="+"/><Var mode="+"/><Var mode="-"/></Atom></signature></Rule> Implemen<Rule key=“#r1" style="reasoning"> <if>... </if> tation <then> <Atom><Rel>getPapers</Rel><Var>Type</Var><Var>Status</Var><Var>Papers</Var></Atom> </then></Rule> Zhili Zhao, Kia Teymourian, Adrian Paschke, Harold Boley and Tara Athan: Loosely-Coupled and Event-Messaged Interactions with 7 Reaction RuleML 1.0 in Rule Responder, 6th International Rule Challenge at RuleML 2012, Montpellier, France, August 27-29, 2012

9. Descriptive and Qualifying Metadata<Assert key="#module1”> <meta>  <Atom><Rel iri="dc:creator"/><Ind>Adrian Paschke</Ind></arg></Atom> </meta> <meta> Descriptive <Time type="&ruleml;TimeInstant"><Data xsi:type="xs:date">2011-01-01</Data></Time> Metadata </meta> <meta> <Atom><Rel>source</Rel><Ind>./module1.prova</Ind></Atom> </meta><qualification>  <Atom>  <Rel>valid</Rel> <Interval type="&ruleml;TimeInstant"> Qualifying <Time type="&ruleml;TimeInstant"><Data xsi:type="xs:date">2011-01-01</Data></Time> Metadata <Time type="&ruleml;TimeInstant"><Data xsi:type="xs:date">2012-01-01</Data></Time> </Interval> </Atom></qualification><Rulebase key="#innermodule1.1"> <meta>…</meta> <qualification> … </qualification> <Rule key=“#rule1”> <meta>…<meta> <qualification> … </qualification> </Rule> <Rule key=“#rule2”> <meta>…<meta> <qualification> … </qualification> </Rule> </Rulebase></Assert> 8

10. Dynamic Views with Scopes and Guards<Assert key="#module1”> <meta>  <Atom><Rel iri="dc:creator"/><Ind>Adrian Paschke</Ind></arg></Atom> </meta> <qualification>  <Atom>  <Rel>valid</Rel> <Interval type="&ruleml;TimeInstant"> <Time type="&ruleml;TimeInstant"><Data xsi:type="xs:date">2011-01-01</Data></Time> <Time type="&ruleml;TimeInstant"><Data xsi:type="xs:date">2012-01-01</Data></Time> </Interval> </Atom> </qualification><Rulebase key="#innermodule1.1"> <!– the rule base scopes apply to all knowledge in the rule base --> <scope> <!– scope : only knowledge authored by „Adrian Paschke“ --> <Atom><Rel iri="dc:creator"/><Ind>Adrian Paschke</Ind></arg></Atom> Outer </scope> Scope <scope> <!– scope : only valid knowledge; validity value will be bound to variable --> <Atom><Rel>valid</Rel><Var>Validity</Var></Atom> </scope> <Rule key=“#rule1”> Inner <scope> <Atom><Rel>source</Rel><Ind>./module1.prova</Ind></Atom></scope> Scope <guard> <!-- guard on the validity: “current date during validity of module1” + <Operator type="&ruleml;During"><Expr iri="…getDate()“/><Var>Validity</Var></Operator> Guard </guard> <if> … </if> <then> </then> </Rule> </Rulebase> Adrian Paschke and Harold Boley: Rule Responder: Rule-Based Agents for the Semantic-Pragmatic Web, in Special 9</Assert> Issue on Intelligent Distributed Computing in International Journal on Artificial Intelligence Tools (IJAIT), V0l. 20,6, 2011

11. Quick Overview: Reaction RuleML Dialects *+ variants and alternatives• Derivation RuleML (if-then)* – Time, Spatial, Interval, Situation (+ algebra operators)• KR RuleML (if-then or on-if-do) * – Happens(@type), Initiates, Terminates, Holds, fluent• Production RuleML (if-do) * – Assert, Retract, Update, Action• ECA RuleML (on-if-do) * – Event, Action, + (event / action algebra operators)• CEP RuleML: (arbitrary combination of on, if, do) – Receive, Send, Message Adrian Paschke, Harold Boley, Zhili Zhao, Kia Teymourian and Tara Athan. Reaction RuleML 1.0: Standardized Semantic 10 Reaction Rules, 6th International Conference on Rules (RuleML 2012), Montpellier, France, August 27-31, 2012

12. Example: Interval-based Processing and Reasoning – Interval-based Event Calculus semantics (model-theory + proof theory) based on time intervals modeled as fluents I: Tinterval x Fl  {true, false} – Example: D = A;(B;C) (consolidation: derive event D from sequence composition) T1 T2 T3 T4 A B C Composition [A,A] [B,C] [[A,A],[B,C]] Consolidation D = [A,C] – Example: derived situation from complex event detection (consolidation: initiate situation1 by event D) initiates(D,situation1,T). holdsAt(situation1, t5)? => yesPaschke,A.:ECA-LP/ECA-RuleML:A Homogeneous Event-Condition-Action Logic Programming Language CoRR abs/cs/0609143:(2006)Adrian Paschke, Alexander Kozlenkov, Harold Boley: A Homogeneous Reaction Rule Language for Complex Event Processing, In Proc. 2nd International Workshop onEvent Drive Architecture and Event Processing Systems (EDA-PS 2007) at VLDB 2007 11Kia Teymourian, Adrian Paschke: Semantic Rule-Based Complex Event Processing. RuleML 2009: 82-92Paschke, A., Bichler, M.: SLA Representation, Management and Enforcement - Combining Event Calculus, Deontic Logic, Horn Logic and Event Condition ActionRules, E-Technology, E-Commerce, E-Service Conference (EEE05), Hong Kong, 2005.

13. Example: KR RuleML – AI Event / Action Reasoning <Rule style="active"> <on> <Event> <Ind> d </Ind> </Event></on> <do> <Initiates > <fluent> <Situation> initiates(d,s1,T). <Ind> s1 </Ind> </Situation> </fluent> </Initiates> </do> </Rule> <Rule style="reasoning" > <if> <And> <Holds> <fluent> <Situation><Var >s1</Var><Situation> holdsAt(s1,t5). </fluent> <at>< Time ><Var>t5</Var></Time></at> </Holds> </And> </if> <then> … </then> </Rules> 12

14. Selected Reaction RuleML Algebra Operators• Action Algebra Succession (Ordered Succession of Actions), Choice (Non-Determenistic Choice), Flow (Parallel Flow), Loop (Loops), Operator (generic Operator)• Event Algebra Sequence (Ordered), Disjunction (Or) , Xor (Mutal Exclusive), Conjunction (And), Concurrent , Not, Any, Aperiodic, Periodic, AtLeast, ATMost, Operator (generic Operator)• Interval Algebra (Time/Spatio/Event/Action/… Intervals) During, Overlaps, Starts, Precedes, Meets, Equals, Finishes, Operator (generic Operator)• Counting Algebra Counter, AtLeast, AtMost, Nth, Operator (generic Operator)• Temporal operators Timer, Every, After, Any, Operator (generic Operator)• Negation operators Adrian Paschke, Harold Boley, Zhili Zhao, Kia Teymourian and Tara Athan. Reaction RuleML 1.0: Standardized Semantic 13 Reaction Rules, 6th International Conference on Rules (RuleML 2012), Montpellier, France, August 27-31, 2012

15. Reaction RuleML Metamodel and External Ontologies + Data - Examples<Quantifier type="&ruleml;Forall"> == <Forall><Operator type="&ruleml;And"> == <And><Operator type="&ruleml;Conjunction"> == <Conjunction><Negation type="&ruleml;InflationaryNegation"> == <Naf><Action type="&ruleml;Assert"> == <Assert><Action type="&ruleml;Retract"> == <Retract><Event type="&ruleml;SimpleEvent"> == <Atom> … </Atom><Event type="ibm:CommonBaseEvent"> == IBM CBE<Operator type="snoop:Squence"> == Snoop Algebra == <Operator type="&ruleml;Sequence"> == <Sequence><Ind iri="person.xml#xpointer(//Person/LastName[1]/text())"/><Action iri="BPEL.xml#xpointer(//invoke[@name=checkHotel])"> 14

16. Reaction RuleML Metamodel Top Level Ontologies General concepts such as space, time, event, action and their properties and relations Spatio Action Ontology Ontology Situation Process Agent Temporal Event Ontology Ontology Ontology Ontology OntologyVocabularies related Vocabularies relatedto specific domains to generic tasks orby specializing the Task activities by Domainconcepts introduced Activities specializing the Ontologiesin the top-level Ontologies concepts introduced inontology the top-level ontology Specific Application E.g. ontologies describing roles user/application played by domain entities while Ontologies perfoming application activities ontologies Teymourian, K., Coskun, G., and Paschke, A. 2010. Modular Upper-Level Ontologies for Semantic Complex Event Processing. In Proceeding of the 2010 Conference on Modular ontologies: Proceedings of the Fourth international Workshop (WoMO 2010) O. Kutz, J. Hois, 15 J. Bao, and B. Cuenca Grau, Eds. Frontiers in Artificial Intelligence and Applications. IOS Press, Amsterdam, The Netherlands, 81-93.

17. Time Top Ontology Metamodel Time Time TypesTime Instant Time Interval 16

18. Interval Top Ontology Metamodel Interval Interval Types Empty Degene Left Closed Left Open Left Left Right RightInterval rated Right Open Right Closed Open Closed Closed Open Interval Interval Interval Interval Interva Interva Interva l l l 17

19. Event Top Ontology Metamodel Event Event TypesSimple Event Complex Event 18

20. Action Top Ontology Metamodel Action Action TypesAtomic Action Complex Action 19

21. Situation Top Ontology Metamodel Situation Situation Types Heterogeneous Fluent Homogenous Situation Situation Domain ontologiesDynamic Time Frequency State Process Iterative Habitual Change Frame Situation Situation Situation Situation SituationSituation Situation 20

22. Example: Situation Top Ontology Metamodel Metadata Pattern Properties Properties hasValue Situation Situation Situation Properties Content Descriptions (time, location, participants, …) use the other hasProperties hasContent top ontologies Situation Situation Types Heterogeneous Homogenous Situation Situation Domain ontologiesDynamic Time Frequency State Process Iterative Habitual Change Frame Situation Situation Situation Situation SituationSituation SituationTrafficLight Within5 Rings3 LaysOn He He He Situation Changes Minutes Times TheFloor Runs Coughs Smokes Individuals 21

23. Example - Event Metamodel(for defining Event Types as Instances of the Metamodel Event Class) Defined Event Types Event Class Definition 22

24. Extended Event Metamodel Ontology (with computational properties and complex value definitions) 23

25. The Generic Syntax Pattern of An Event references to external ontologies <Event @key @keyref @iri @type>  <meta>  </meta> <scope>  </scope>  <evaluation> </evaluation> <signature>  </signature>  <qualification>  </qualification> <quantification>  </quantification> <oid>  </oid> <content>  </content> </Event> 24

26. Complex Event Pattern Definition<Event key=“#ce2" type="&ruleml;ComplexEvent"> <signature>  <Sequence> <signature> <Event type="&ruleml;SimpleEvent"> <signature><Event>...event_A...</Event></signature> </Event> </signature> <signature><Event type="&ruleml;ComplexEvent" keyref="ce1"/></signature> Event <signature> Pattern <Event type="cbe:CommonBaseEvent" iri="cbe.xml#xpointer(//CommonBaseEvent)"/> </signature> defining </Sequence> Event </signature> Templates</Event> signature<Event key=“#ce1"> <signature>  <Concurrent> <Event><meta><Time>...t3</Time></meta><signature>...event_B</signature></Event> <Event><meta><Time>...t3</Time></meta><signature>...event_C</signature></Event> </Concurrent> </signature></Event> Event<Event key=“#e1” keyref=“#ce2”><content>…</content></Event> Instance 25

27. Rule Interface and Rule Implementation <Rule style="active"> <evaluation> <Profile> e.g. selection and consumptions policies </Profile> </evaluation> <signature> Interface <Event key="#ce2"> (Note in reaction … see example from the previous slide rules the signature </Event> is the event pattern </signature> which detects the event instances) <on> <Event keyref="#ce2"/> <!– use defined event pattern for detecting events --> </on> <do> <Assert safety="transactional">  <formula> <Atom> <op><Rel>likes</Rel></op> Implementation <arg><Ind>John</Ind></arg> <arg><Var>X</Var></arg> </Atom> </formula> </Assert> </do> </Rule> 26

28. Execution Semantics(can be defined in the <evaluation> semantics element)1. Definition – Definition of event/action pattern e.g. by event algebra – Based on declarative formalization or procedural implementation – Defined over an atomic instant or an interval of time, events/actions, situation, transition etc.2. Selection – Defines selection function to select one event from several occurred events (stored in an event instance sequence e.g. in memory, database/KB) of a particular type, e.g. “first”, “last” – Crucial for the outcome of a reaction rule, since the events may contain different (context) information, e.g. different message payloads or sensing information3. Consumption – Defines which events are consumed after the detection of a complex event – An event may contribute to the detection of several complex events, if it is not consumed – Distinction in event messaging between “multiple receive” and “single receive” – Events which can no longer contribute, e.g. are outdated, should be removed4. Execution – Actions might have an internal effect i.e. change the knowledge state leading to state transition from (pre-)condition state to post-condition state – The effect might be hypothetical (e.g. a hypothetical state via a computation) or persistent (update of the knowledge base), – Actions might have an external side effect Adrian Paschke: The Reaction RuleML Classification of the Event / Action / 27 State Processing and Reasoning Space CoRR abs/cs/0611047: (2006) 27

29. EPTS - Reference Architecture: Functional View Definition, Modeling, (continuous) Improvement Event Reaction Assessment, Routing, Prediction, Discovery, Learning (Pattern, Control, Rule, Query, RegEx.etc) Event Process Monitoring, Control 0..* State Management Complex Event Detection Event and Complex Event Consolidation, Composition, Aggregation 0..* Event Analysis Analytics, Transforms, Tracking, Scoring, Rating, Classification 0..* Event Preparation Identification, Selection, Filtering, Monitoring, Enrichment 0..* Event Production Event Consumption Publication, Dashboard, Apps, Retrieval External ReactionDesign time Run time Administration Adrian Paschke, Paul Vincent, Alexandre Alves, Catherine Moxey: Tutorial on advanced design patterns in event processing. 28 DEBS 2012: 324-334; http://www.slideshare.net/isvana/epts-debs2012-event-processing-reference-architecture-design-patterns-v204b

30. Coupling Approaches for (Distributed) Reaction RuleML Bases• Strong coupling direct implementation – Interaction through a stable interface KB – API call is hard coded Computer• Loose coupling – Resilient relationship between two or Agent / Service Interface more systems or organizations with Reaction RuleML Message Interchange some kind of exchange relationship Rule KB1 – Each end of the transaction makes its Request requirements explicit, e.g. as an Rule Service 0.0000 … Consumer interface description, and makes few Response assumptions about the other end 0.0000 Rule KBn• Decoupled – de-coupled in time using (event) messages (e.g. via Message-oriented Event Streams (ordered events) Middleware (MoM)) new auto pay account login logout – Often asynchronous stateless deposit withdrawalnew auto pay communication (e.g. publish- deposit transfer Event book logoutaccount login enquiry subscribe or CEP event detection) account balance Cloud incident request new auto pay account login enquiry enquiry (unordered events) depositwithdrawalnew auto pay transfer account login enquiry deposit logout 29 activity history

31. Reaction RuleML Rule interchange (on the PIM level) Rules Reaction RuleML serialize de-serial. Rules doc Rules Ontologies Rule (ODM, OWL, Rule system 1 RDF-S, CL) system 2 <XML doc> Data serialize (or others) de-serial. Data Application A data Application B 30

32. Send and Receive Messaging• Send a message• Send @directive (oid,protocol,agent,content)• Receive a message• Receive @directive (oid,protocol,agent,content) –oid is the conversation identifier (enabling also subconverstations) –protocol: protocol definition (high-level protocols and transport prot.) –agent: denotes the target or sender of the message –@directive: pragmatic context, e.g. FIPA Agent Communication Language (ACL) primitives –content: Message payload 31

33. Messaging Reaction Rules<Rule> … <do><Send><oid>xid1</oid> …query1 </Send></do> <do><Send><oid>xid2</oid> …query2 </Send></do> <on><Receive><oid>xid2</oid> …response2 </Receive></on> <if> prove some conditions, e.g. make decisions on the received answers </if> <on><Receive><oid>xid1</oid> …response1 </Receive></on> ….</Rule> Note: The „on“, „do“, „if“ parts can be in arbitrary combinations to allow for a flexiblesee Prova rule engine workflow-style logic with subconversationswww.prova.ws and parallel branching logic 32

34. Reaction - Routing Prova: Example with Agent (Sub-) Conversations rcvMsg(XID,esb,From,query-ref,buy(Product) :- routeTo(Agent,Product), % derive processing agent % send order to Agent in new subconversation SID2 sendMsg(SID2,esb,Agent,query-ref,order(From, Product)), % receive confirmation from Agent for Product order rcvMsg(SID2,esb,Agent,inform-ref,oder(From, Product)). % route to event processing agent 1 if Product is luxury routeTo(epa1,Product) :- luxury(Product). % route to epa 2 if Product is regular routeTo(epa2,Product) :- regular(Product). % a Product is luxury if the Product has a value over … luxury(Product) :- price(Product,Value), Value >= 10000. % a Product is regular if the Product ha a value below … regular(Product) :- price(Product,Value), Value < 10000. corresponding serialization with ReactionProva3, http://prova.ws RuleML <Send> and <Receive> 33Slides: http://www.slideshare.net/isvana/epts-debs2012-event-processing-reference-architecture-design-patterns-v204b

35. Reaction RuleML Messaging•<Message directive="" >• <oid>  </oid>• <protocol>  </protocol>• <sender>  </sender>• <receiver>  </receiver>• <content>  </content>•</Message> • oid is the conversation identifier (enabling also subconverstations) • protocol: protocol definition (high-level protocols and transport prot.) • agent (send, receiver): denotes the target or sender of the message • @directive: pragmatic context, e.g. FIPA ACL primitives • content: Message payloadAdrian Paschke and Harold Boley: Rule Responder: Rule-Based Agents for the Semantic-Pragmatic Web, in Special Issue on Intelligent Distributed Computing in International Journal on Artificial Intelligence Tools (IJAIT), V0l. 20,6, 2011 34

36. Loosley-Coupled Communication via Messages to Agent Interface FIPA ACL directive <Message directive="acl:query-ref"> <oid> <Ind>conversation1</Ind> </oid> <protocol> <Ind>esb</Ind> </protocol> <sender> <Ind>Agent1</Ind> </sender> <receiver> <Ind>Agent2</Ind> </receiver> <content> <Atom> Interpreted by Agent 2 as <Rel>likes</Rel> query according to <Ind>John</Ind> ACL:query-ref <Ind>Mary</Ind> </Atom> Note: the receiver „Agent2“ needs to specify an </content> appropriate <signature> for </Message> „likes“ • Event Message is local to the conversation state (oid) and pragmatic context (directive) 35

37. Message Driven RoutingProva : Event Routing in Event-Driven WorkflowsrcvMsg(XID,Process,From,event,["A"]) :- Task1 fork_b_c(XID, Process). B D A C Task2fork_b_c(XID, Process) :- E @group(p1) rcvMsg(XID,Process,From,event,["B"]), execute(Task1), sendMsg(XID,self,0,event,["D"]).fork_b_c(XID, Process) :- @group(p1) rcvMsg(XID,Process,From,event,["C"]), execute(Task2), sendMsg(XID,self,0,event,["E"]).fork_b_c(XID, Process) :- % OR reaction group "p1" waits for either of the two event message handlers "B" or "C" and terminates the alternative reaction if one arrives @or(p1) rcvMsg(XID,Process,From,or,_). Prova3, http://prova.ws 36 Slides: http://www.slideshare.net/isvana/epts-debs2012-event-processing-reference-architecture-design-patterns-v204b

38. Coupling Approaches for (Distributed) Reaction RuleML Bases• Strong coupling direct implementation – Interaction through a stable interface KB – API call is hard coded Computer• Loose coupling – Resilient relationship between two or Agent / Service Interface more systems or organizations with Reaction RuleML Message Interchange some kind of exchange relationship Rule KB1 – Each end of the transaction makes its Request requirements explicit, e.g. as an Rule Service 0.0000 … Consumer interface description, and makes few Response assumptions about the other end 0.0000 Rule KBn• Decoupled – de-coupled in time using (event) messages (e.g. via Message-oriented Event Streams (ordered events) Middleware (MoM)) new auto pay account login logout – Often asynchronous stateless deposit withdrawalnew auto pay communication (e.g. publish- deposit transfer Event book logoutaccount login enquiry subscribe or CEP event detection) account balance Cloud incident request new auto pay account login enquiry enquiry (unordered events) depositwithdrawalnew auto pay transfer account login enquiry deposit logout 37 activity history

39. Decoupled Interaction: Event Messaging Reaction Rule … sendMsg(SID,stream,“S&P500“, inform, tick((name, N),(price, P), (time, T)) … publish Event Stream decoupled {(Name, “OPEL”)(Price, 45)(Volume, 2000)(Time, 1) } {(Name, “SAP”)(Price, 65)(Volume, 1000) (Time, 2)} consum / subscribe rcvMult(SID,stream,“S&P500“, inform, tick(Name^^car:Major_corporation,P^^currency:Dollar, T^^time:Timepoint)) :- …<further processing of semantic event>. {(OPEL, is_a, car_manufacturer), (car_manufacturer, build, Cars), (Cars, are_build_from, Metall), Semantic (OPEL, hat_production_facilities_in, Germany),Knowledge Base (Germany, is_in, Europe) (OPEL, is_a, Major_corporation), (Major_corporation, have, over_10,000_employees)} Prova3, http://prova.ws 38 Slides: http://www.slideshare.net/isvana/epts-debs2012-event-processing-reference-architecture-design-patterns-v204b

40. Example: Semantic BPM and BAM with Rule Responder Adrian Paschke: A Semantic Rule and Event Driven Approach for Agile Decision-Centric Business Process Management . ServiceWave 2011: 254-267Business Process 39

41. Highly Modular Relax NG for Reaction RuleML• Reaction RuleML is a monotonic language extension of Deliberation RuleML – Every valid Deliberation RuleML document is a valid Reaction RuleML document• Deliberation RuleML 1.0 is specified by a modular, monotonic Relax NG schema, released 2012-04-03 – Reaction RuleML 1.0 can be specified by new Relax NG modules that may be freely-combined with (some or all) Deliberation RuleML modules – E.g.: modules/Rule.rnc, modules/scope.rnc, modules/event.rnc, modules/action.rnc, …• MYNG (http://ruleml.org/1.0/myng) provides a GUI for building a customized Deliberation RuleML Relax NG schema from these modules – MYNG can be extended to the Reaction RuleML schema modules 40

42. Summary of Selected Reaction RuleML Features• Different Rule Families and Types (DR, KR, PR, ECA, CEP)• Support for Event/Action/Situation… Reasoning and Processing• Rule Interface and Implementation for Distributed Knowledge• Knowledege Life Cycle Management, Modularization and Dynamic Views (metadata, scopes, guards, situations/fluents, update actions)• Decoupled event messaging and loosley-coupled send/receive interaction against rule (KB) interface within conversations, coordination/negotiation protocols and pragmatic directives• Different detection, selection and consumption semantics (profiles)• Different algebras (Event, Action, Temporal,…)• Specialized Language Constructs – Intervals (Time, Event), situations (States, Fluents)• Top Level Meta Model for semantic Type Definitions and predefined Types in Top-Level Ontologies• External (event) query languages, external data and ontology models• ... 41

43. Selected References• Adrian Paschke, Harold Boley, Zhili Zhao, Kia Teymourian and Tara Athan. Reaction RuleML 1.0: Standardized Semantic Reaction Rules, 6th International Conference on Rules (RuleML 2012), Montpellier, France, August 27-31, 2012• Harold Boley, Adrian Paschke and Omair Shafiq: RuleML 1.0: The Overarching Specification of Web Rules , in Semantic Web Rules, Proceedings of RuleML-2010, Lecture Notes in Computer Science, 2010, Volume 6403/2010, 162-178.• Paschke, A., Boley, H., Kozlenkov. A., Craig, B.: Rule Responder: RuleML-Based Agents for Distributed Collaboration on the Pragmatic Web, 2nd International Conference on the Pragmatic Web, Tilburg, Netherlands, 2007.• Adrian Paschke and Harold Boley: Rule Responder: Rule-Based Agents for the Semantic-Pragmatic Web, in Special Issue on Intelligent Distributed Computing in International Journal on Artificial Intelligence Tools (IJAIT), V0l. 20,6, 2011• Adrian Paschke, Paul Vincent, Alexandre Alves, Catherine Moxey: Advanced design patterns in event processing. DEBS 2012: 324-334; http://www.slideshare.net/isvana/epts-debs2012-event-processing-reference-architecture-design- patterns-v204b• Adrian Paschke, Alexander Kozlenkov: Rule-Based Event Processing and Reaction Rules. RuleML 2009: 53-66• Adrian Paschke, Paul Vincent, Florian Springer: Standards for Complex Event Processing and Reaction Rules. RuleML America 2011: 128-139; http://www.slideshare.net/isvana/ruleml2011-cep-standards-reference-model• Teymourian, K., Coskun, G., and Paschke, A. 2010. Modular Upper-Level Ontologies for Semantic Complex Event Processing. In Proceeding of the 2010 Conference on Modular ontologies: Proceedings of the Fourth international Workshop (WoMO 2010) O. Kutz, J. Hois, J. Bao, and B. Cuenca Grau, Eds. Frontiers in Artificial Intelligence and Applications. IOS Press, Amsterdam, The Netherlands, 81-93.• Kia Teymourian, Adrian Paschke: Semantic Rule-Based Complex Event Processing. RuleML 2009: 82-92• Adrian Paschke, Alexander Kozlenkov, Harold Boley: A Homogeneous Reaction Rule Language for Complex Event Processing, In Proc. 2nd International Workshop on Event Drive Architecture and Event Processing Systems (EDA-PS 2007) at VLDB 2007; http://arxiv.org/pdf/1008.0823v1.pdf• Adrian Paschke: ECA-LP / ECA-RuleML: A Homogeneous Event-Condition-Action Logic Programming Language CoRR abs/cs/0609143: (2006); http://arxiv.org/ftp/cs/papers/0609/0609143.pdf• Adrian Paschke: ECA-RuleML: An Approach combining ECA Rules with temporal interval-based KR Event/Action Logics and Transactional Update Logics CoRR abs/cs/0610167: (2006); http://arxiv.org/ftp/cs/papers/0610/0610167.pdf• Paschke, A., Bichler, M.: SLA Representation, Management and Enforcement - Combining Event Calculus, Deontic Logic, Horn Logic and Event Condition Action Rules, E-Technology, E-Commerce, E-Service Conference (EEE05), Hong Kong, 2005.• Adrian Paschke, Martin Bichler: Knowledge representation concepts for automated SLA management. Decision Support Systems 46(1): 187-205 (2008)• Adrian Paschke: RBSLA A declarative Rule-based Service Level Agreement Language based on RuleML. CIMCA/IAWTIC 42 2005: 308-314

44. Thank you ! Questions? 4343