F13: Model Driven Interoperability and MDA Industrial ... · F13: Model Driven Interoperability and...

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INF5120 – Modellbasert

Systemutvikling

F13: Model Driven Interoperability and MDA

Industrial Evolution (Genova example)

Lecture 03.05.2010

Arne-Jørgen Berre

ICT 2

INF5120 - Lecture plan - 2010 1: 25/1: Introduction to MBSU, MDA, OO and Service/SOA modeling, Overall EA, 4 parts: MDE/SSS/MS/MDI

(AJB)

Part I: MDE – Model Driven Engineering

2: 1/2: MDE I: Metamodeling. DSL and UML profiles, MDA technologies (XMI, Eclipse, EMF/GMF) (AJB/BRE)

Part II: SSS – Service Science and Service/SOA technologies

3: 8/2: SSS I: Service science (top down) - Service and SOA Technologies (bottom up) (AJB)

Part I continued: MDE – Model Driven Engineering

4: 15/2: MDE II: Model transformations with MOFScript, ATL and other technologies (GO/JO)

5 :22/2: MDE III: Code generation with MOFScript, ATL and other technologies (GO/JO)

Part III: MOS – Modeling of Services - with SoaML

6: 1/3: MOS I: Business Process Modeling (CIM) - with BPMN 2.0, and BMM, EA with UPDM (AJB)

7: 8/3: MOS II: Soaml, UML2 and SysML, Modelio SOA and Scope, –Collaboration and Component models (AJB)

8: 15/3: MOS III: SoaML (PIM) and Requirements modeling , CIM->PIM and SoaML (AJB)

9: 22/3: MOS IV: Method Engineering and SPEM / EPF - for Service systems (BRE)

EASTER

Part IV – Model Driven Interoperability

10: 12/4: MS V: SOA and Service Design, MDA and ADM - Intro to MDI (AJB )

11: 19/4: MDI I: Semantic Web with Ontologies and Model Driven Interoperability (TIR)

12: 26/4: MDI II: Semantic Services and Model Driven Interoperability (TIR)

13: 3/5: MDE IV: Model Driven Interoperability and Industrial Evolution of MDA/MDE (AJB), Knut Sagli/ESITO)

14: 10/5: Course summary and preparation for Exam 31/5 (AJB)

Exam: May 31st, 2010 (Monday), 0900-1200 (3 hours)

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Agenda

Model Driven Interoperability MODELS 2010, Oslo, 3-8. October 2010

Ref. 2 papers on MDI

Industrial evolution of MDA/MDE

Modeling tools: MagicDraw, Enterprise

Architect, Modeio, IBM Software Architect,

Microsoft Domain Specific Languages, Oracle,

...

Guest lecture, ESITO – Genova, Knut Sagli

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MODELS 2010, Oslo, 3-8. October 2010

See: http://models2010.ifi.uio.no Student volunteers ?

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Model Driven Interoperability

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Current MDA Interoperability Architecture

CIM/EM

models

PIM

System

models

PSM

System

models

System

Ref.

ontologySemantic

annotation

Semantic

annotation

Semantic

annotation

CIM/EM

models

PIM

System

models

PSM

System

models

System

Semantic

annotation

Semantic

annotation

Semantic

annotation

Sem.mapping

Technical

mapping

Interoperability

executionIF IF

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Run-time

Sem

Annot

Set

#2

InternetSem

Rec

Rules

#2

Local

Software

&

Data

SwApp#1

Local

Software

&

Data

SwApp#2Sem

Annot

Set

#1

Sem

Rec

Rules

#1

ReferenceOntology

Architecture for semantic annotation and reconciliation

Reconciliation

Design-time

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Model Driven Service

Interoperability through use of

Semantic Annotations

I-ESA 2009 paper

Arne-Jørgen Berre

Fangning Liu

Jiucheng Xu

Brian ElvesæterSINTEF ICT

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Model Driven Interoperability through

Semantic Annotations using SoaML

and ODM

JiuCheng Xu*, ZhaoYang Bai*,

Arne J.Berre*, Odd Christer Brovig**

INCOM’09, Moscow,

3. June. 2009

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Contents

Introduction

Description of EMPOWER and MEMPOWER

EMPOWER Project

MEMPOWER Project

Comparison Semantic mappings

Conclusion & Further work

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EMPOWER

an innovative framework for interoperability between

enterprise systems

a flexible and extensible architecture

a system environment

System Interoperability LayerInteroperable

Enterprise

Service

Designer

Wrapper Definition

and CustomizationWeb Services

Repository

Semantic Adaptation Layer

(2)Services

Semantic

Annotator(SAWSDL

)

(3)Ontology

Handling

Utilities(OWL

)

(5)Transformation

s Creator

Interoperable

Enterprise Service

Wrapper

Mediator Services

Web ServerSemantic Services

Registry

Transformations

Repository

Model

Repository

Legacy System Wrappers

Legacy Systems

(1)WSDL, OWL-S, WSML(4)Semantic

Map

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a Model Driven variant of EMPOWER,

Compare with advantages and disadvantages of Model

Driven Interoperability

MEMPOWER

System Interoperability Layer

SemaphoreWrapperWeb Services

Repository

(1)Model Mapping (SoaML)

Legacy System Wrappers

Legacy Systems

(4)Model

Map

Semantic Adaptation Layer

(2)SAM (3)ODM

(5)Model

Transformation

ServicesWrapper

Mediator

ServerSemantic

Services

Registry Transformation

s Repository

Model

Repository

Ontology Definition Meta-model is a

family of MOF meta-models,

mappings between those meta-

models, and a set of profiles that

enable ontology modeling through the

use of UML-based tools.

SoaML describes the services models. The

Model Mapping in the MEMPOWER

includes transformations from models to

ontology and ontology to models.

Semantic Annotation Model editor

is used to relate different PIM models

and ontology. It is used to annotate

the SoaML model with Ontology.

Model Transformation Services

support the runtime lifting and lowering

transformations among messages and

ontologies based on the Model Map.

Model Map stores mapping rules.

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The EMPOWER Enterprise

Interoperable Services Semantic Map

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Semantic

Adaptation

Architecture

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PIM level use of Ontology mappings

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Use of SoaML for PIM modeling

16

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SAM – Semantic Annotations tools

17

(SASO: semantic annotation tool using SoaML and ODM)

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Ontology example

18

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Address Ontology

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Address in Source and UML

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“Address” in the source and target

transformation rules

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“Address” transformations from

source.xml and target.xmi

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SAM editor realized in tree views

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Interface of demo

A simple example of class

annotations on the PIM

level

Annotations

Ontology is represented as a

structured and classified tree view. It

shows the properties and relationships

between those classes.

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<soaml:Class name="POMessage” saName=“PurchaseOrderMessage”soaml:sterotype="messageType">

</soaml:Class><soaml:Class name="Customer" saName=“Customer”

soaml:sterotype="DataType"><soaml:Attribute name="customerId" saName=“hasCompanyRegNo”

type="String" modifier="public" /><soaml:Attribute name="name" saName=“hasComanyName”

type="Name" modifier="public" /><soaml:Attribute name=“address“ saName=“hasAddress”

type="String" modifier="public" /><soaml:Attribute name=“creditScore" type="Integer" modifier="public" />

</soaml:Class>

After annotating and exporting the model, you will get the file with a additional attribute. The annotations are displayed in red.

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Semantic Mapping

1. Ontology-based mapping on the PSM-Level (EMPOWER)

2. Direct mapping on the PSM-Level

3. Ontology-based mapping on the PIM level(MEMPOWER)

4. Direct mapping on the PIM level

1 2 3 4

Approach Ontology-based PSM

Direct mapping PSM

Ontology-based PIM

Direct mapping PIM

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Example: Address

Address in

Target.xsd has only

one elements:

Address

Address in Source.xsd

is divided into three

elements: Address,

Place, and Province

Address in Ontology is

divided into three

elements: Address,

Region, and Province

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1.PSM: Ontology-based Annotation based on ontology on the PSM-level

--Annotate source.xml and target.xml using Ontology

Ontology

Source.xml

Address

annotation

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2.PSM: Direct Mapping Mapping without ontology on the PSM-level

--Map between source.xml and target.xml (xsl:easy)

Target.xml

Source.xml

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3.PIM: Ontology-based 1.Transformation From PSM level to PIM level

--Generate sources.uml and target.uml from schemas (HyperModel Designer

3.1)

Address in Source.xsd

Address in Source.uml

corresponds to

Source.xsd

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3.1)

2.Mapping Between Models based on ontology on the PIM level

Step 1: Generate meta-models

of models and ontology using EMF

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3.1)


Step 2:Create mapping rules from

source to ontology, and ontology to

target using ATL

Ontology-

Target

Source-

Ontology

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--Generate sources.uml and target.uml from schema (HyperModel Designer

3.1)


Step3: Transform source into

ontology and ontology into target

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Transformation Between Models without ontology on the PIM level

--Use Semaphore tool to map source to target

4.PIM: Direct Mapping

Source.uml

Target.uml

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Conclusion

Ontology -based mapping (S-O-T) VS Direct mapping (S-T) on

the PIM level

2N vs N²

Ontology

model

Model A

Model C

Model F

Model E

Model D

Model B

Model A

Model D

Model C

Model C

Model C

Model B

Mapping between all model pairs will result in N-squared

mappings

Mapping between each model and ontology will result a linear growth of number of mappings

Standard

Ontology

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Conclusion

Mapping PIM-Level VS PSM-Level

Ontology-basedPSM

Direct mapping PSM

Ontology-basedPIM

Direct mappingPIM

Mapping 2N N² 2N N²

StandardOntology

Y N Y N

PlatformIndependent

N N Y Y

Multi-source documents

Input

N N Y Y

Multi-target documents

Output

N N Y Y

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Conclusion & Further work

Conclusion

Ontology-based semantic annotations reduces mapping times

from N-squared to 2N, but cost is a standard ontology.

Model Driven approach supports the interoperability independent

from platform technologies, compared to a platform specific

technical approach.

Further work

Implement multiple industrial use cases with five scenarios for

comparing EMPOWER and MEMPOWER.

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Another example of Ontology-

based Service: Message

Reconciliation

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Ad hoc reconciliation vs Ontology-based

Reconciliation

Ad-Hoc

Based on ad hoc adapters between pair of partners

Not scalable respect to the growing of the number of partners

Ontology-based

Highly independent solution, the semantic annotation does

not depend on the other business partners

Highly scalable, the complexity of the Semantic Annotation

does not depend on the cardinality of the partners

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Ontology-based reconciliation

Local Schema Local Schema

Enterprise A Enterprise B

Semantic

Annotation

Semantic

Annotation

Reconciliation

Rules

Customized

MRE

Customized

MRE

Reconciliation

Rules

Local Data Local Data

Design phase

Run-time phase

Interch.

Repres.

ReferenceOntology

FWD transf BWD transf

BWD transf FWD transf

SW App SW App

Semantic Mediation

and Reconciliation

Platform

Semantic Mediation

and Reconciliation

Platform

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Lossless and Lossy Annotations

Lossless SA: when the annotation fully captures the intended

meaning

A Local Schema (LS) element corresponds exactly to a concept in

the RO

The meaning of a LS element can be precisely derived from

concepts in the RO

Lossy SA: when the annotation fails to fully representing the

intended meaning

The meaning of a LS element does not have a matching concept in

the ontology, nor the possibility of deriving it, since:

- the intended meaning is outside the scope of the RO

- The LS elem is not sufficiently refined (i.e., it does not match the

accuracy level of e ontology) [underspecification]

- The LS element presents a level of refinement not deemed useful

[overspecification]

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Example of Mismatch

Structuring

Purchase Order

• Order_Number

• Order_Date

• Buyer_Info

– Name

– Address

• Street_Name

• Street_Num

• City_Post_Code

• Country

– Telephone

• Products_Info

– Product_Code

– Description

– Quantity

– Price (unitary)

• Currency (Dollar, Euro, Pound)

• Charge

• RequestedDeliveryDate

Sale Order

• Date

• Organization_Name

• Contact_Person

• Location– Street_Address

– City

– LoCode

– Country

• Phone_Number– Area_Code

– Number

– Ext

• Client_Order_Number

• Order_Lines– Product_Code

– Description

– Quantity

– Price (total per line)

• Currency (USD, Euro, Yen)

• Total

EnterprA (Buyer) EnterprB (Supplier)

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Ontology-based Reconciliation Approach

Address

Street SnumCountryZip_Code

Location

Street_Address

Reference Ontology

City

Street_Name

Street_Number

City-Post_Code

Country

LoCode

Country

City

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Local Schema (LS) Reference Ontology (RO)

Purchase Order (PO)

…

Address

…

City-Post_Code: literal

Address [

…

City : literal

Zip_Code: literal ]

Structuring Clash

Example of actual reconciliation

LS.PO.Address.City-Post_Code =:

RO. Address.City AND RO.Address.Zip_Code

Semantic Annotation

unpack(LS.PO.Address.City-Post_Code, “-”)

(RO.Address.City, RO. Address.Zip_Code)

ReconciliationRule

{“Rome - 00185”} {“Rome”, “00185”}Run-time Reconciliation

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…

<xsd:element name=“Address”>

<xsd:complexType>

<xsd:sequence>

<xsd:element name=“Street_Name”

type=“xsd:string”/>

<xsd:element

name=“Street_Number”

type=“xsd:positiveInteger”/>

<xsd:element name=“City-

Post_Code” type=“xsd:string”/>

<xsd:element name=“Country”

type=“xsd:string”>

</xsd:sequence>

</xsd:complexType>

</xsd:element>

…

…<owl:Class rdf:ID=“Address”/>

<owl: DatatypeProperty rdf:ID=“Street”><rdfs:domain rdf:resource=“Address”/><rdfs:range rdf:resource=“&xsd;string”/>

</owl: DatatypeProperty><owl: DatatypeProperty rdf:ID=“Snum”>

<rdfs:domain rdf:resource=“Address”/><rdfs:range rdf:resource=“&xsd;positiveInteger”/>

</owl: DatatypeProperty><owl: DatatypeProperty rdf:ID=“City”><rdfs:domain rdf:resource=“Address”/><rdfs:range rdf:resource=“&xsd;string”/>

</owl: DatatypeProperty><owl: DatatypeProperty rdf:ID=“Zip_Code”>

<rdfs:domain rdf:resource=“Address”/><rdfs:range rdf:resource=“&xsd;string”/>

</owl: DatatypeProperty><owl: DatatypeProperty rdf:ID=“Country”>

<rdfs:domain rdf:resource=“Address”/><rdfs:range rdf:resource=“&xsd;string”/>

</owl: DatatypeProperty>…

Local Schema (XML Schema) Reference Ontology (OWL)

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From Semantic Annotation to Transformation Rules

order.has_orderHeader.has_buyerInfo.has_organisationInfo.has_contactPerson.has_name

PurchaseOrder_BOD.relTo_Buyer.relTo_ContactPerson. hasPart _FirstName

PurchaseOrder_BOD.relTo_Buyer.relTo_ContactPerson.hasPart _Surname

>:

AIDIMA

order

RO

orderorder

order

header

order

header

products

info

products

info

supplier

info

supplier

info

buyer

info

buyer

infoorg

info

org

info

contact

person

contact

person namename

orgNameorgName

address

details

address

details

product

record

product

recorddescriptiondescription

productCodeproductCode

quantityquantity

……

……

……

buyerOrderNumberbuyerOrderNumber

……

……

orderorder

order

header

order

header buyer

info

buyer

infoorg

info

org

info

contact

person

contact

person namename

orderorder

order

header

order

header buyer

info

buyer

infoorg

info

org

info

contact

person

contact

person namename

PurchaseOrderPurchaseOrder

OrderLineOrderLine

IDID IssueDateIssueDate

BuyerBuyer

SupplierSupplier

Contact

Person

Contact

Person

SurnameSurnameFirstNameFirstName

ProductProduct

LinePriceLinePriceQuantityQuantity

BODBOD

AA AA

BOD

AA

BA

CABA

BA

AA

AA

DescriptionDescriptionAA

NameName

AA

YearYearAA

MonthMonthAA


BuyerBuyer

Contact

Person

Contact

Person



BuyerBuyer

Contact

Person

Contact

Person


Split

SSAX

SPLITorder.has_orderHeader.has_buyerInfo.has_organisationInfo.has_contactPerson.has_name

INTO

PurchaseOrder_BOD.relTo_Buyer.relTo_ContactPerson.hasPart_FirstName

PurchaseOrder_BOD.relTo_Buyer.relTo_ContactPerson.hasPart_Surname

Forward

Transf Rule

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An example of Transformation Rule in the Jena2 syntax

NameSplitting:

[(?x0 rdf:type ai:order)

(?x0 ai:has_orderHeader ?x1) (?x1 rdf:type ai:orderHeader)

(?x1 ai:has_buyerInfo ?x2) (?x2 rdf:type ai:buyerInfo)

(?x2 ai:has_organizationInfo ?x3) (?x3 rdf:type ai:organizationInfo)

(?x3 ai:has_contactPerson ?x4) (?x4 rdf:type ai:contactPerson)

(?x4 ai:has_name ?x5)]

[(?x0 rdf:type ro:PurchaseOrder_BOD)

(?x0 ro:relTo_Buyer ?x2) (?x2 rdf:type ro:Buyer_BA)

(?x2 ro:relTo_ContactPerson ?x4) (?x4 rdf:type ro:ContactPerson_BA)

Split(?x4, “ ”, ?y1, ?y2, 'http://www.w3.org/2001/XMLSchema#string')

(?x4 ro:hasPart_FirstName ?y1) (?x4 ro:hasPart_Surname ?y2)]

SPLITorder.has_orderHeader.has_buyerInfo.has_organisationInfo.has_contactPerson.has_name

INTO

PurchaseOrder_BOD.relTo_Buyer.relTo_ContactPerson.hasPart_FirstName

PurchaseOrder_BOD.relTo_Buyer.relTo_ContactPerson.hasPart_Surname

Forward

Transf Rule

Rule in the

Jena2 syntax

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Conclusion and outlook BMM can be used to support discussions on

Organisational interoperability

Support for semantics with ontologies and mediation is

available now

Short term benefit can be gained in the area of services

for semantic interoperability – through the use of

ontologies, and use of mappings and transformations for

information and service interoperability

i.e. – start here from an industrial perspective, establish

ontologies, use these directly or mediate through semantic

annotation.

Semantic Web Services and Service-oriented Semantic

Architectures (SESA) is a promising future technology

Longer term benefits can be expected related to matching

goals with services for process and service composition

and process interoperability 48

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