alexis aubry, mario lezoche. enterprise information systems: a proposition for conceptualisation and...

[ Alexis AUBRY / Mario LEZOCHE]

Enterprise Information Systems: a proposition for conceptualisation and interoperability evaluation

Alexis AUBRYMario LEZOCHE

2010, 10th November


Contents

• Background• Enterprise Systems Models Semantic

interoperability• Proposed methodology

• Fact Oriented Semantic conceptualisation• Semantics Structure enactment: the semantic

blocks

• Future works

2


Background

• Product-centric paradigm in the Enterprise Information Systems:

3

Informational flow

Physical flowProduct

Problem of synchronisation between the informational and the physical flows

Business

Manufacturing

ProductCRAN(Morel et al., 2003)

Fully integrated system (CIM)

The product is considered as an information system (Tursi, 2009) The informational and physical flows are synchronised via the system product

From one monolithic Information System… … to a set of heterogeneous and autonomous ISs aggregated into one SoIS (Auzelle, 2009)

System of information systems (SoIS)

CRM ERP

MESSCE

APS

CRM

ERP

APS

MESSCE


Background

4

• Two constats:1. Increasing complexity of the information flows

(quantity, diversity…)

2. Distribution of the information in the whole supply chain Enterprises use an increasing number of software applications These enterprise information systems have to interoperate… … in order to achieve the global performances for the full

manufacturing processes.

Product

CRM ERP

MESSCE

APS

Semantic interoperability


Semantic interoperability

5

• Definition (Whitman et al., 2006)Two Information Systems are interoperable if they are able:i) to share specified information and,ii) to operate using that information according to a shared

semanticsiii) in order to realise a specified mission in a given context.

Main issues:

1.Studying the semantic gap between different Information System models and concepts2.Highlighting the non-interoperation effects

Product

CRM ERP

MESSCE

APS


Proposed Methodology

CRM IS1

IS2IS

APS

Conceptualisation

Conceptual Model 1

PRODUCT+Weight-val[0..1]:int+Weight-un[0..1]:UNIT

IS2

IS1

Conceptual Model 2

PRODUCT

WEIGHT+val[1]:int+un[1]:UNIT

0..11

Non-interoperation

Problems: 1. The two models are not made by the same expert. The modelling experiences are not the same Several possible conceptual representations (UML, MERISE, ORM…)

2. Majority of conceptual models are built a posterioriImplementation-based functionalities and constraints can disturb the semantics of the ISs: hiding, overloading...

Fact-oriented modelling

6


IS2


CRM IS1

IS2IS

APS

Conceptual Model 2

Conceptual Model 1

Conceptualisation

Product

Weight-un

Weight-val

0..1Has

1

0..1Has

1

Fact-oriented Conceptual

Model 1


IS1

PRODUCT


0..11


Model 2

PRODUCT

WEIGHT

val

un

1Has 1 1 Has1

1 0..1

Fact oriented modelling

Non-interoperation

- Using Object-Role Modelling which is the most popular fact-oriented approach.

7


IS2

Product

Weight-un

Weight-val

0..1Has

1

0..1Has

1


8

CRM IS1

IS2IS

APS

Conceptual Model 2

Conceptual Model 1

Conceptualisation Fact oriented modelling


Model 1


Model 2


IS1

PRODUCT


0..11

PRODUCT

WEIGHT

val

un

1Has 1 1 Has1

1 0..1

Semantics structure enactment

Non-interoperation

Semantic Blocks of Model 2

- Pointing the concepts that are mandatory to semantically define a given concept in each fact-oriented conceptual model



IS2

Product

Weight-un

Weight-val

0..1Has

1

0..1Has

1


9

CRM IS1

IS2IS

APS

Conceptual Model 2

Conceptual Model 1



Model 1


Model 2


IS1

PRODUCT


0..11

PRODUCT

WEIGHT

val

un

1Has 1 1 Has1

1 0..1


Non-interoperation



Semantic gap evaluation

Semantic gap¬ IS1 ⊓ IS2

Common semanticsIS1⊓ IS2

Semantic gapIS1 ⊓¬ IS2

Semantic gapIS1 ⊔ IS2

- Using the semantic blocks for identifying the semantic gaps between the two information systems- Highlighting the non-interoperation effects


IS2

Product

Weight-un

Weight-val

0..1Has

1

0..1Has

1


10

CRM IS1

IS2IS

APS

Conceptual Model 2

Conceptual Model 1



Model 1


Model 2


IS1

PRODUCT


0..11

PRODUCT

WEIGHT

val

un

1Has 1 1 Has1

1 0..1


Non-interoperation









Fact Oriented Semantic Conceptualisation(Lexical enactment and primary structuring)

For each Information System

1. Reverse engineering from the database structure to the first conceptual model (automatic)

2. Conceptual model examination by the domain expert according to the good practices in the enterprise and the data (manual)

3. Fact-oriented transformation (automatic)

11


Reverse Engineering :DB structure -> First Conceptual Model

12

The reverse engineering process will extract the database’s entities, attributes, relationships from a given database to a conceptual model (in our case described in UML format).

Reverse Engineering

1° Step 2° Step

Physical Relational Conceptual

DBMS ER UML


Reverse Engineering:two different use cases

13

Flexnet

Sage X3

Relational DiagramRelational Diagram Class DiagramClass Diagram

Class DiagramClass Diagram


Expertwork

- Modifying the multiplicities of the attributes- Adding explicit names to the concepts, the

attributes and the associations- Others operations to fit the conceptual model to

the “real” use of the Enterprise Information System.

Automaticwork

Manual Expert work:Adonix Sage example

14

Sage X3Relations,

Primary keys, trigger, index…

Manual « cleaning » of this conceptual model by the domain expert according to the good practices in the enterprise and the data

Software interface


Fact Oriented Transformation

Fact oriented transformation (automatic)

1. Making out the attributes: building an association between the concepts and its attributes with respecting the multiplicities

2. Transforming the different types of association in simple association by respecting some rules and adding, eventually, some semantics annotation

15

C1

A1 [1]A2 [0..1]

C1

A1

A21

1

0..1

1


Fact Oriented Transformation:Associations reduction 1

16

C1

A1A2

C2

A3

C1

Aggregation

C21 *

C1 C21 *

{C1 aggregates: C2}

Generalization

C2

A3

1

1

C1

A2A1

11

11

1 *

{C1 generalizes: C2}

1

1

1

1


Fact Oriented Transformation:Associations reduction 2

17

C2C1

Composition

C21 *

C1 C21 *

{C1 isComposedOf: C2}

C1

C3

C2C1

C3

Association Class

1

1

1

*

*

*

{C3 isTypeOf: associationClass(C1,C2)}


Fact Oriented Transformation:Adonix Sage example

18

Facility

Product

1

*

Third Part Reference [1]Creation Date [1]Article Code [1]Quality Card [1]Replacement Article [1]UCEE-US Coefficient [0..1]Export Number [0..1]

RIB Number [1]Default Addres [1]Associated Site [0..1]

Product

Third PartReference

CreationDate

ArticleCode

QualityCard

ReplacementArticle

UCEE-USCoefficient

Export Number

Facility

RIBNumber

DefaultAddress

AssociatedSite

1

11

11 1

1

1

0..1

0..1

1 1 1 1

1

1

0..11

1

1

1

*


IS2

Product

Weight-un

Weight-val

0..1Has

1

0..1Has

1


19

CRM IS1

IS2IS

APS

Conceptual Model 2

Conceptual Model 1



Model 1


Model 2


IS1

PRODUCT


0..11

PRODUCT

WEIGHT

val

un

1Has 1 1 Has1

1 0..1


Non-interoperation









Semantics Structure enactment(Semantic blocks)

20

C1

C2

C3

C4

1..*

C1C2

*

1C1C3*

1..*

C3C40..1

C5

1..*C2C5_20..1

1 C2C5_1 *

C8

1

C5C8

*

C6

1..*

C3C61..*

C7

1C4C7_21

1..*

C6C7

*

1

C6C80..1

*

C5C6

0..1

*

C2C3

*

1

C4C7_11..*

0..1

C1C8

0..1

*

C8C8

1

A semantic block, denoted as B(c), and associated with a concept c, represents the set of concepts necessary to the minimal semantics definition of the concept c.

• Semantic block: definition


C1

C2

C3

C4

1..*

C1C2

*

1C1C3*

1..*

C3C40..1

C5

1..*C2C5_20..1

1 C2C5_1 *

C8

1

C5C8

*

C6

1..*

C3C61..*

C7

1C4C7_21

1..*

C6C7

*

1

C6C80..1

*

C5C6

0..1

*

C2C3

*

1

C4C7_11..*

0..1

C1C8

0..1

*

C8C8

1

21

B(C2)

A semantic block, denoted as B(c), and associated with a concept c, represents the set of concepts necessary to the minimal semantics definition of the concept c.

• Semantic block: definition


[ Alexis AUBRY / Mario LEZOCHE] 22

C1

C2

C3

C4

1..*

C1C2

*

1C1C3*

1..*

C3C40..1

C5

1..*C2C5_20..1

1 C2C5_1 *

C8

1

C5C8

*

C6

1..*

C3C61..*

C7

1C4C7_21

1..*

C6C7

*

1

C6C80..1

*

C5C6

0..1

*

C2C3

*

1

C4C7_11..*

0..1

C1C8

0..1

*

C8C8

1

𝐶1 𝐶2

𝐶3

𝐶5 𝐶8

𝐶4 𝐶7

𝐶6

Associated directed graph

• An associated semantic-relationships graph



C1

C2

C3

C4

1..*

C1C2

*

1C1C3*

1..*

C3C40..1

C5

1..*C2C5_20..1

1 C2C5_1 *

C8

1

C5C8

*

C6

1..*

C3C61..*

C7

1C4C7_21

1..*

C6C7

*

1

C6C80..1

*

C5C6

0..1

*

C2C3

*

1

C4C7_11..*

0..1

C1C8

0..1

*

C8C8

1

𝐶1 𝐶2

𝐶3

𝐶5 𝐶8

𝐶4 𝐶7

𝐶6





C1

C2

C3

C4

1..*

C1C2

*

1C1C3*

1..*

C3C40..1

C5

1..*C2C5_20..1

1 C2C5_1 *

C8

1

C5C8

*

C6

1..*

C3C61..*

C7

1C4C7_21

1..*

C6C7

*

1

C6C80..1

*

C5C6

0..1

*

C2C3

*

1

C4C7_11..*

0..1

C1C8

0..1

*

C8C8

1

𝐶1 𝐶2

𝐶3

𝐶5 𝐶8

𝐶4 𝐶7

𝐶6



B(C2)

Theorem 1A concept c’ is included in the semantic block of c if and only if there exists a directed path from c to c’ in the associated directed graph.



• A three-phases procedure to build the semantic blocksi. Building the associated

semantic-relationships graph,

ii. simplifying the graph using graph theory properties and using Kosaraju-Sharir’s algorithm,

iii. and building the semantic block associated with each aggregated node. Deducing the semantic block of each concept.

𝐶1 𝐶2

𝐶3

𝐶5 𝐶8

𝐶4 𝐶7

𝐶6

C1

C2

C3

C4

1..*

C1C2

*

1C1C3*

1..*

C3C40..1

C5

1..*C2C5_20..1

1 C2C5_1 *

C8

1

C5C8

*

C6

1..*C3C6

1..*

C7

1C4C7_21

1..*

C6C7

*

1

C6C80..1

*

C5C6

0..1

*

C2C3

*

1

C4C7_11..*

0..1

C1C8

0..1

*

C8C8

1

𝑆𝐶𝐶1 𝑆𝐶𝐶2

𝑆𝐶𝐶3 𝑆𝐶𝐶4



Future works

26

IS2

Product

Weight-un

Weight-val

0..1Has

1

0..1Has

1

CRM IS1

IS2IS

APS

Conceptual Model 2

Conceptual Model 1



Model 1


Model 2


IS1

PRODUCT


0..11

PRODUCT

WEIGHT

val

un

1Has 1 1 Has1

1 0..1


Non-interoperation




(Yahia, 2011)






Enterprise Information Systems: a proposition for conceptualisation and interoperability evaluation

Alexis AUBRYMario LEZOCHE

Thank you for your attention !

2010, 10th November

alexis aubry, mario lezoche. enterprise information systems: a proposition for conceptualisation and...

Technology

aps conceptual model

unit is2 is1 conceptual

factoriented modelling

product weight va

unit is1 prod uct weight

conceptualisation fact

methodology crm is1

methodology fact