Semantically-Enabled Business Process Management

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  1. 1. Semantically-Enabled Business Process Management Ontology PSIG Meeting, June 18th, 2015 OMG Technical Meeting, Berlin, Germany Adrian Paschke Corporate Semantic Web (AG-CSW) Institute for Computer Science, Freie Universitaet Berlin
  2. 2. Overview Semantic Business Process Management Ontologies in BPM - Examples Rules in BPM - Examples Events in BPM - Examples Summary Key Benefits of SBPM
  3. 3. Information Sources: Knowledge Management: Workflows Process Knowledge Semantik Information Events/Actions & Process Context Relations & Interpretation Content BPM BPMBPM BPM Workflow Workflow Literature Colleagues Databases Experts Product Contents Business Processes Semantic Business Processes Management
  4. 4. Semantic + BPM Semantic Business Process Management Business Process + Semantic Technologies BPM + Ontologies and Vocabularies BPM + Rules for Decision + Reaction Logic BPM + Semantic Data and Event Processing
  5. 5. Main Semantic Technologies 1. Ontologies Ontologies described the conceptual knowledge of a domain (concept semantics) 2. Rules Describe derived conclusions and reactions from given information (rule inference) 3. Semantic Data & Content Semantically enriched data and events Partner Customer is a equal with Client if premium(Customer) then discount(10%) on alarm do notify
  6. 6. Partner Customer is a same as Client Semantic Annotaion BPEL BPMN Ontologies in BPM - Example Domain Ontology Domain Ontology
  7. 7. Rules in BPM - Example if premium(Customer) and regular(Product) then discount(Customer, Product, 5%) if premium(Customer) and luxury(Product) then discount(Customer, Product, 10%) if spending(Customer, > 5000 EUR) then premium(Customer) If Then Spending Customer >5000 premium Rules, e.g. SBVR, RuleML Decision Tables e.g. DMN
  8. 8. Event Stream {(Name, OPEL)(Price, 45)(Volume, 2000)} {(Name, SAP)(Price, 65)(Volume, 1000)} CEP Query: Buy shares of companies which have production facilities in Europe and produce products from iron and have more than 10,000 employees and are at the moment in restructuring phase and their price/volume have been increasing continuously in the past 5 minutes. {(OPEL, is_a, automobile_company), (automobile_company, build, Cars), (Cars, built_from, Iron), (OPEL, has_production_facilities_in, Germany), (Germany, is_in, Europe) (OPEL, is_a, Major_corporation), (Major_corporations, have, over_10,000_employees), (OPEL, is_in, reconstructing_phase)} Knowledge Base A B C Buy 1 Buy 2 D E Semantic CEP in BPM - Example
  9. 9. Selected Benefits of Semantics in BPM Semantic Transformations e.g., from BPMN into e.g. BPEL into Web Services Semantic Mapping / Interchange e.g., from on BPMN / BPEL model into another in cross-domain / cross-organizational business processes Semantic Execution / Interpretation e.g., ontological understanding of the business process e.g. rule-based & event-based decisions and reactions e.g. formal semantic for consistency and validation
  10. 10. Ontologies + BPM Examples
  11. 11. Top Level Reaction RuleML Ontologies General concepts such as space, time, event, action and their properties and relations Temporal Ontology Action Ontology Process Ontology Agent Ontology Situation Ontology Domain Ontologies Vocabularies related to specific domains by specializing the concepts introduced in the top-level ontology Task Activities Ontologies Vocabularies related to generic tasks or activities by specializing the concepts introduced in the top-level ontology Application Ontologies Specific user/application ontologies E.g. ontologies describing roles played by domain entities while perfoming application / service activities Spatio Ontology Event Ontology Source: Reation RuleML Metamodel Modular Ontology Model for SBPM
  12. 12. Example - Event Metamodel (for defining Event Types of the Reaction RuleML Metamodel Event Class) Defined Event Types Event Class Definition Integration of existing domain ontologies by defining their properties and values in an event classes in the Metamodel Domain ontologies
  13. 13. Semantic Extension of Information Entities Utilize corporate or domain ontology concepts to define information flow on a non-technical conceptual level suitable for business process experts due to formal nature consistent link between the business or conceptual level and underlying technical information models can be derived formal domain information models are foundation for semantic mediation between heterogeneous conceptualizations used by different organizations or domains
  14. 14. Semantic Business Process Modeling Cross-Organizational Business Process Mapping Heterogeneous Corporate/Domain Ontologies
  15. 15. Mapping heterogeneous semantic sub-graphs (ontologies) Mapping with semantic bridges (rules) polymorph classification preserving object identity Semantic Mediation between heterogeneous Information Entities
  16. 16. Example Mediated Business Process
  17. 17. Semantic Business Process Execution with Semantic Web Services Business Processes Enterprise Application Components Services Hardware Web Service Application Service Using Application Semantic Service Interface ITSM (Rules) ITSM (Rules) Semantic SLA Non-functional Properties Response Time Delay / Availability Resource Utilization Functionality Guarantees Pricing /Policies Rights & Obligations Escalation Service Customer/User Service Provider Business Vocabulary (Ontologies) Business Vocabulary (Ontologies) Semantic Web Service OWL-S (former DAML-S), WSDL-S RBSLA ( SAWSDL SWWS / WSMF WSMO / WSML Meteor-S SWSI SWS Approaches
  18. 18. Semantic CEP: Ontologies (cont.) Better understanding of situations (states) e.g., a process is executing when it has been started and not ended Better understanding of the relationships between events e.g., temporal, spatial, causal, .., relations between events, states, activities, processes e.g., a service is unavailable when the service response time is longer than X seconds and the service is not in maintenance state Data becomes meaningful information and declarative knowledge while conforming to an underlying formal semantics e.g., automated semantic mediation between different heterogeneous domains and abstraction levels e.g. enabling greater automation of discovery, selection, invocation, composition, monitoring, and other service management tasks
  19. 19. Rules + BPM Examples
  20. 20. Rules Technology Users employ rules to express what they want, the responsibility to interpret this and to decide on how to do it is delegated to an interpreter Represent knowledge in a way that is understandable by the business, but also executable by rule engines, thus bridging the gap between business and technology IBM ILog Drools Prova PRR RuleML RIF SBVRCIM PIM PSM DMN
  21. 21. Rules-enabled BPEL Application BPEL run-time BRMS (Business Rules Management System) events, facts results CEP Logic Reaction Logic Decision Logic Constraints Rule Inference Service Rule Repositories Vocabularies / Semantic Ontology Models Rule Interchange Ontology / Model Mapping Rule-based BPEL+ (Semantic BPEL)
  22. 22. Orchestrated BPEL + Choreography Rule Workflow Rules-enabled BPEL Application BPEL run- time BRMS (Business Rules Management System) events , facts results CEP Logic Reaction Logic Decision Logic Constraints Rule Inference Service % receive query and delegate it to another party rcvMsg(CID,esb, Requester, acl_query-ref, Query) :- responsibleRole(Agent, Query), sendMsg(Sub-CID,esb,Agent,acl_query-ref, Query), rcvMsg(Sub-CID,esb,Agent,acl_inform-ref, Answer), ... (other goals)... sendMsg(CID,esb,Requester,acl_inform-ref,Answer). Rules can be used to implement choreography workflows as subprocesses in the orchestration BPEL flow Workflows might span several communicating (messaging) rule inference services Prova rule engine
  23. 23. Prova Rule Example: Rule-based Routing 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 XML serialization with Reaction RuleML and rulechaining rulechaining
  24. 24. Semantic BPM: Rules Rule Inference Services and Agents can be dynamically invoked from a BPM process. Dynamic processing Intelligent routing Validation of policies within process Constraint checks Ad-hoc Workflow Policy based task assignment Various escalation policies Load balancing of tasks Business Activity Monitoring Alerts based on certain policies and complex event processing (rule- based CEP) Dynamic processing based KPI reasoning
  25. 25. Event-Driven Semantic BPM Examples
  26. 26. Knowledge Value of Events Proactive actions Value of Events At eventBefore the event Some time after event e.g. 1 hour Real-Time Late reaction or Long term report Historical Event Post-Processing Time The CEP market is expected to grow from $1,005.0 million in 2014 to $4,762.0 million in 2019. This represents a CAGR of 36.5% from 2014 to 2019. ResearchAndMarkets, November 2014
  27. 27. Comp