Semantically-Enabled Business Process Management

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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 [email protected] http://www.inf.fu-berlin.de/groups/ag-csw/

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

[email protected]

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

Ontologies + BPM

Examples

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

Example Mediated Business Process

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 + BPM

Examples

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

Event-Driven Semantic BPM

Examples

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

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Design time

Event AnalysisAnalytics, Transforms, Tracking,

Scoring, Rating, Classification

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Sta

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see.: Adrian Paschke, Paul Vincent, Alexandre Alves, Catherine Moxey: Advanced design patterns in event processing. ACM DEBS 2012: 324-334;

Where CEP impacts BPMN

Source: P. Vincent, A. Paschke

Summary

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