Semantically-Enabled Business Process Management
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Transcript of Semantically-Enabled Business Process Management
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
http://www.inf.fu-berlin.de/groups/ag-csw/
Overview
Semantic Business Process Management
Ontologies in BPM - Examples
Rules in BPM - Examples
Events in BPM - Examples
Summary Key Benefits of SBPM
InformationSources:
Knowledge Management:
Workflows
Process Knowledge
Semantik
Information
Events/Actions & Process Context
Relations & Interpretation
Content
BPM BPMBPM
BP
M
WorkflowWorkflow
Literature Colleagues Databases Experts Product Contents
Business Processes
Semantic Business Processes Management
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
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 dataand events
Partner
Customer
is a
equal with
Client
if premium(Customer)
then discount(10%)
on alarm do notify
Partner
Customer
is a
same as
Client
<invokepartnerLink=“MakeSpecialOffer" portType=“Customer" operation=“Make" inputVariable=“Offer" outputVariable=“Accept"/>
Semantic
Annotaion
BPEL
BPMN
Ontologies in BPM - Example
Domain Ontology
Domain Ontology
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 Tablese.g. DMN
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
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
Top Level Reaction RuleML Ontologies
General concepts such as space, time, event, action and their properties and relations
Temporal Ontology
Action Ontology Process
OntologyAgent
OntologySituation Ontology
Domain Ontologies
Vocabularies related to specific domainsby 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: ReationRuleML Metamodel
Modular Ontology Model for SBPM
Example - Event Metamodel (for defining Event Types of the Reaction RuleML Metamodel Event Class)
Defined Event Types
Event ClassDefinition
Integration of existing domain ontologies by defining theirproperties and values in an event classes in the Metamodel
Domain ontologies
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
Semantic Business Process ModelingCross-Organizational Business Process Mapping
Heterogeneous
Corporate/Domain
Ontologies
Mapping
heterogeneous
semantic sub-graphs
(ontologies)
Mapping with
semantic bridges
(rules)
polymorph
classification
preserving object
identity
Semantic Mediation betweenheterogeneous Information Entities
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 SLANon-functional
Properties
Response Time
Delay / Availability
Resource Utilization
Functionality
Guarantees
Pricing /Policies
Rights & Obligations
Escalation
Service
Customer/UserService Provider
Business
Vocabulary (Ontologies)Business
Vocabulary (Ontologies)
Semantic Web Service
•OWL-S (former DAML-S),
•WSDL-S
•RBSLA (http://rbsla.ruleml.org)
•SAWSDL
•SWWS / WSMF
•WSMO / WSML
•Meteor-S
•SWSI
•…
SWS Approaches
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
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
ILogDrools Prova
PRR RuleML RIF
SBVRCIM
PIM
PSM
DMN
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)
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 http://prova.ws
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 <Send> and <Receive>
rule
chain
ing
rule
chain
ing
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
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
Complex Events – What are they?
Complex Events are aggregates, derivations, etc. of Simple Events
Complex EventsSimple Events
Simple EventsSimple Events
Simple Events
Event
Patterns
Complex Event Processing (CEP) will enable, e.g.
– Detection of state changes based on observations
– Prediction of future states based on past behaviours
Realt Time
Data
Processing
Data
Complex Event Processing
Event Cloud(unordered events)
new auto payaccount login
account logindeposit
withdrawal
logout
account balance
transfer
depositnew auto pay
enquiryenquiry
logout
new auto payaccount login
account login
deposit
activity history
withdrawal
logouttransfer
deposit new auto pay
enquiry
enquiry
book
requestincident A
B
C
CEP is about complex event detection and reaction Efficient (near real-time) processing of large numbers of events
Detection, prediction and exploitation of relevant complex events
Situation awareness, track & trace, sense & respond
Co
mp
lex
Eve
nts
Event Streams(ordered events)
Patterns, Rules
Event Processing Technical Society Reference Architecture: Functional View
Event Production Publication, Retrieval
Even
t Pro
cess M
on
itorin
g, C
on
trol
Event PreparationIdentification, Selection, Filtering,
Monitoring, Enrichment
Complex Event DetectionConsolidation, Composition,
Aggregation
Event ReactionAssessment, Routing, Prediction,
Discovery, Learning
Event Consumption Dashboard, Apps,External Reaction
Run time Administration
Even
t a
nd
Co
mp
lex E
ven
t(P
att
ern
, Contr
ol, R
ule
, Q
uery
, RegEx.e
tc)
Defi
nit
ion
, M
od
eli
ng
, (co
nti
nu
ou
s) I
mp
rovem
en
t
Design time
Event AnalysisAnalytics, Transforms, Tracking,
Scoring, Rating, Classification
0..*
0..*
0..*
0..*
Sta
te M
an
ag
em
en
t
see.: Adrian Paschke, Paul Vincent, Alexandre Alves, Catherine Moxey: Advanced design patterns in event processing. ACM DEBS 2012: 324-334;
Summary: Semantic BPM
Complementary technologies: Semantic technologies + BPM technologies
Knowledge representation and declarative decision and reaction logic is integrated into the context of BPM
Ontologies for events, processes, states, actions, and other concepts that relate to change over time support rules and decision+reaction logic that govern processes or react to events
(Complex) event data becomes declarative knowledge while conforming to an underlying formal semantics
Rule-based reasoning over situations and states and automated execution of adaptive reactions
supports automated semantic translation, interchange, reuse, execution and adaption of semantic BPM models
across major BPM & BRMS & CEP vendors
in distributed cross-organizational business processes
on top of enterprise-relevant knowledge
Literature Adrian Paschke: A Semantic Rule and Event Driven Approach for Agile Decision-Centric Business Process Management.
ServiceWave 2011: 254-267
Adrian Paschke, Kia Teymourian: Rule Based Business Process Execution with BPEL+, In Proceedings of I-Semantics '09, pages
588-601
Nils Barnickel, Johannes Böttcher, Adrian Paschke:
Semantic Mediation of Information Flow in Cross-Organizational Business Process Modeling. SBPM 2010: 21-28
Adrian Paschke: Reaction RuleML 1.0 for Rules, Events and Actions in Semantic Complex Event Processing, RuleML 2014, Springer
LNCS, Prague, Czech Republic, August, 18-20, 2014
Zhili Zhao, Adrian Paschke: A Formal Model for Weakly-structured Scientific Workflows. SWAT4LS 2013
Kia Teymourian, Gökhan Coskun, Adrian Paschke: Modular Upper-Level Ontologies for Semantic Complex Event Processing. WoMO
2010: 81-93
Adrian Paschke: The Reaction RuleML Classification of the Event / Action / State Processing and Reasoning Space. CoRR
abs/cs/0611047 (2006), http://arxiv.org/ftp/cs/papers/0611/0611047.pdf
Nils Barnickel, Johannes Böttcher, Adrian Paschke:
Incorporating semantic bridges into information flow of cross-organizational business process models. I-SEMANTICS 2010
Adrian Paschke, Alexander Kozlenkov: A Rule-based Middleware for Business Process Execution. Multikonferenz
Wirtschaftsinformatik 2008 (MKWI 2008).
Adrian Paschke, Paul Vincent, Alexandre Alves, Catherine Moxey: Advanced design patterns in event processing. DEBS 2012: 324-
334;
Adrian Paschke and Harold Boley. Rule responder: Rule-based agents for the semantic-pragmatic web. International Journal on
Artificial Intelligence Tools, 20(6):1043-1081, 2011.
Kia Teymourian, Olga Streibel, Adrian Paschke, Rehab Alnemr, Christoph Meinel: Towards Semantic Event-Driven Systems. NTMS
2009
Zhili Zhao, Adrian Paschke: Rule Agent-Oriented Scientific Workflow Execution, S-BPM ONE 2013, Springer-Verlag, pp. 109-122,
Deggendorf, Germany, March 11-12, 2013
Zhili Zhao, Adrian Paschke: Event-Driven Scientific Workflow Execution, Proceedings of Business Process Management Workshops
(BPM’12), Springer Berlin Heidelberg, vol. 132, pp. 390-401, Tallinn, Estonia, 2012
Adrian Paschke, Zhili Zhao: Process Makna - A Semantic Wiki for Scientific Workflows. SWAT4LS 2010
Adrian Paschke, Zhili Zhao: Rule Responder: A Rule-Based Semantic eScience Service Infrastructure. SWAT4LS 2010