This document proposes a methodology for conceptualizing and evaluating semantic interoperability between enterprise information systems. It involves: 1. Conceptualizing each information system using fact-oriented modeling to extract a conceptual model from the database structure. 2. Identifying the "semantic blocks" which represent the minimum concepts needed to semantically define other concepts in each conceptual model. 3. Evaluating the semantic gaps between the two information systems by comparing their semantic blocks and identifying any non-interoperating concepts or relationships. The methodology is intended to help address the increasing complexity of information flows between distributed enterprise systems that need to interoperate by achieving a shared understanding of semantics.

1. [ Alexis AUBRY / Mario LEZOCHE]
Enterprise Information Systems: a proposition for
conceptualisation and interoperability evaluation
Alexis AUBRY
Mario LEZOCHE
2010, 10th
November

2. [ Alexis AUBRY / Mario LEZOCHE]
Contents
• Background
• Enterprise Systems Models Semantic
interoperability
• Proposed methodology
• Fact Oriented Semantic conceptualisation
• Semantics Structure enactment: the
semantic blocks
• Future works
2

3. [ Alexis AUBRY / Mario LEZOCHE]
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
MES
SCE
APS
CR
M
ERP
APS
MESSCE

4. [ Alexis AUBRY / Mario LEZOCHE]
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
MES
SCE
APS
⇒ Semantic
interoperability

5. [ Alexis AUBRY / Mario LEZOCHE]
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
semantics
iii)in order to realise a specified mission in a given context.
Main issues:
1.Studying the semantic gap between different
Information System models and concepts
2.Highlighting the non-interoperation effects
Product
CRM ERP
MES
SCE
APS

6. [ Alexis AUBRY / Mario LEZOCHE]
Proposed Methodology
CRM IS1
IS2
IS
APS
Conceptualisation
Conceptual
Model 1
PROD UCT
+Weight-val[0..1]:int
+Weight-un[0..1]:UNIT
IS2
IS1
Conceptual
Model 2
PROD UCT 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
posteriori
⇒Implementation-based functionalities and
constraints can disturb the semantics of the ISs:
hiding, overloading...
⇒ Fact-oriented modelling
6

7. [ Alexis AUBRY / Mario LEZOCHE]
IS2
Proposed Methodology
CRM IS1
IS2
IS
APS
Conceptual
Model 2
Conceptual
Model 1
Conceptualisation
Product
We ight-un We ight-va l
0..1
Has
1
0..1
Has
1
Fact-oriented
Conceptual
Model 1
PR OD UCT
+Weight-val[0..1]:int
+Weight-un[0..1]:UNIT
IS1
PROD UCT WEIGHT
+val[1]:int
+un[1]:UNIT
0..11
Fact-oriented
Conceptual
Model 2
PROD UCT WEIG HT
va l un
1
Has 1 1 Has
1
1 0..1
Fact oriented
modelling
Non-interoperation
- Using Object-Role
Modelling which is the
most popular fact-
oriented approach.
7

8. [ Alexis AUBRY / Mario LEZOCHE]
IS2
Product
We ight-un We ight-va l
0..1
Has
1
0..1
Has
1
Proposed Methodology
8
CRM IS1
IS2
IS
APS
Conceptual
Model 2
Conceptual
Model 1
Conceptualisation Fact oriented
modelling
Fact-oriented
Conceptual
Model 1
Fact-oriented
Conceptual
Model 2
PR OD UCT
+Weight-val[0..1]:int
+Weight-un[0..1]:UNIT
IS1
PROD UCT WEIGHT
+val[1]:int
+un[1]:UNIT
0..11
PRODUCT WEIGHT
va l un
1
Has 1 1 Has
1
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
Semantic
Blocks of
Model 1

9. [ Alexis AUBRY / Mario LEZOCHE]
IS2
Product
We ight-un We ight-va l
0..1
Has
1
0..1
Has
1
Proposed Methodology
9
CRM IS1
IS2
IS
APS
Conceptual
Model 2
Conceptual
Model 1
Conceptualisation Fact oriented
modelling
Fact-oriented
Conceptual
Model 1
Fact-oriented
Conceptual
Model 2
PR OD UCT
+Weight-val[0..1]:int
+Weight-un[0..1]:UNIT
IS1
PROD UCT WEIGHT
+val[1]:int
+un[1]:UNIT
0..11
PRODUCT WEIGHT
va l un
1
Has 1 1 Has
1
1 0..1
Semantics structure
enactment
Non-interoperation
Semantic
Blocks of
Model 1
Semantic
Blocks of
Model 2
Semantic gap
evaluation
Semantic gap
¬ IS1 ⊓ IS2
Common semantics
IS1⊓ IS2
Semantic gap
IS1 ¬⊓ IS2
Semantic gap
IS1 ⊔ IS2
- Using the semantic
blocks for identifying
the semantic gaps
between the two
information systems
- Highlighting the non-
interoperation effects

10. [ Alexis AUBRY / Mario LEZOCHE]
IS2
Product
We ight-un We ight-va l
0..1
Has
1
0..1
Has
1
Proposed Methodology
10
CRM IS1
IS2
IS
APS
Conceptual
Model 2
Conceptual
Model 1
Conceptualisation Fact oriented
modelling
Fact-oriented
Conceptual
Model 1
Fact-oriented
Conceptual
Model 2
PR OD UCT
+Weight-val[0..1]:int
+Weight-un[0..1]:UNIT
IS1
PROD UCT WEIGHT
+val[1]:int
+un[1]:UNIT
0..11
PRODUCT WEIGHT
va l un
1
Has 1 1 Has
1
1 0..1
Semantics structure
enactment
Non-interoperation
Semantic
Blocks of
Model 1
Semantic
Blocks of
Model 2
Semantic gap
evaluation
Semantic gap
¬ IS1 ⊓ IS2
Common semantics
IS1⊓ IS2
Semantic gap
IS1 ¬⊓ IS2
Semantic gap
IS1 ⊔ IS2

11. [ Alexis AUBRY / Mario LEZOCHE]
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

12. [ Alexis AUBRY / Mario LEZOCHE]
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

13. [ Alexis AUBRY / Mario LEZOCHE]
Reverse Engineering:
two different use cases
13
Flexnet
Sage X3
Relational DiagramRelational Diagram Class DiagramClass Diagram
Class DiagramClass Diagram

14. [ Alexis AUBRY / Mario LEZOCHE]
Expert
work
- 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.
Automatic
work
Manual Expert work:
Adonix Sage example
14
Sage X3
Relations,
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

15. [ Alexis AUBRY / Mario LEZOCHE]
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

16. [ Alexis AUBRY / Mario LEZOCHE]
Fact Oriented Transformation:
Associations reduction 1
16
C1
A1
A2
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

17. [ Alexis AUBRY / Mario LEZOCHE]
Fact Oriented Transformation:
Associations reduction 2
17
C2C1
Composition
C21 *
C1 C2
1 *
{C1 isComposedOf: C2}
C1
C3
C2C1
C3
Association Class
1
1
1
*
*
*
{C3 isTypeOf:
associationClass(C1,C2)}

18. [ Alexis AUBRY / Mario LEZOCHE]
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 Part
Reference
Creation
Date
Article
Code
Quality
Card
Replacement
Article
UCEE-US
Coefficient
Export
Number
Facility
RIB
Number
Default
Address
Associated
Site
1
1
1
1
1 1
1
1
0..1
0..1
1
1 1 1
1
1
0..1
1
1
1
1
*

19. [ Alexis AUBRY / Mario LEZOCHE]
IS2
Product
We ight-un We ight-va l
0..1
Has
1
0..1
Has
1
Proposed Methodology
19
CRM IS1
IS2
IS
APS
Conceptual
Model 2
Conceptual
Model 1
Conceptualisation Fact oriented
modelling
Fact-oriented
Conceptual
Model 1
Fact-oriented
Conceptual
Model 2
PR OD UCT
+Weight-val[0..1]:int
+Weight-un[0..1]:UNIT
IS1
PROD UCT WEIGHT
+val[1]:int
+un[1]:UNIT
0..11
PRODUCT WEIGHT
va l un
1
Has 1 1 Has
1
1 0..1
Semantics structure
enactment
Non-interoperation
Semantic
Blocks of
Model 1
Semantic
Blocks of
Model 2
Semantic gap
evaluation
Semantic gap
¬ IS1 ⊓ IS2
Common semantics
IS1⊓ IS2
Semantic gap
IS1 ¬⊓ IS2
Semantic gap
IS1 ⊔ IS2

20. [ Alexis AUBRY / Mario LEZOCHE]
Semantics Structure enactment
(Semantic blocks)
20
C1
C2
C3
C4
1..*
C1C2
*
1C1C3
*
1..*
C3C4
0..1
C5
1..*C2C5_20..1
1 C2C5_1 *
C8
1
C5C8
*
C6
1..*
C3C6
1..*
C71C4C7_21
1..*
C6C7
*
1
C6C80..1
*
C5C6
0..1
*
C2C3
*
1
C4C7_1
1..*
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

21. [ Alexis AUBRY / Mario LEZOCHE]
C1
C2
C3
C4
1..*
C1C2
*
1C1C3
*
1..*
C3C4
0..1
C5
1..*C2C5_20..1
1 C2C5_1 *
C8
1
C5C8
*
C6
1..*
C3C6
1..*
C71C4C7_21
1..*
C6C7
*
1
C6C80..1
*
C5C6
0..1
*
C2C3
*
1
C4C7_1
1..*
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
Semantics Structure enactment
(Semantic blocks)

22. [ Alexis AUBRY / Mario LEZOCHE] 22
C1
C2
C3
C4
1..*
C1C2
*
1C1C3
*
1..*
C3C4
0..1
C5
1..*C2C5_20..1
1 C2C5_1 *
C8
1
C5C8
*
C6
1..*
C3C6
1..*
C71C4C7_21
1..*
C6C7
*
1
C6C80..1
*
C5C6
0..1
*
C2C3
*
1
C4C7_1
1..*
0..1
C1C8
0..1
*
C8C8
1
�1
�2
�3
�5
�8
�4 �7
�6
Associated
directed graph
• An associated semantic-relationships graph
Semantics Structure enactment
(Semantic blocks)

23. [ Alexis AUBRY / Mario LEZOCHE] 23
C1
C2
C3
C4
1..*
C1C2
*
1C1C3
*
1..*
C3C4
0..1
C5
1..*C2C5_20..1
1 C2C5_1 *
C8
1
C5C8
*
C6
1..*
C3C6
1..*
C71C4C7_21
1..*
C6C7
*
1
C6C80..1
*
C5C6
0..1
*
C2C3
*
1
C4C7_1
1..*
0..1
C1C8
0..1
*
C8C8
1
�1
�2
�3
�5
�8
�4 �7
�6
Associated
directed graph
• An associated semantic-relationships graph
Semantics Structure enactment
(Semantic blocks)

24. [ Alexis AUBRY / Mario LEZOCHE] 24
C1
C2
C3
C4
1..*
C1C2
*
1C1C3
*
1..*
C3C4
0..1
C5
1..*C2C5_20..1
1 C2C5_1 *
C8
1
C5C8
*
C6
1..*
C3C6
1..*
C71C4C7_21
1..*
C6C7
*
1
C6C80..1
*
C5C6
0..1
*
C2C3
*
1
C4C7_1
1..*
0..1
C1C8
0..1
*
C8C8
1
�1
�2
�3
�5
�8
�4 �7
�6
Associated
directed graph
• An associated semantic-relationships graph
B(C2)
Theorem 1
A 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.
Semantics Structure enactment
(Semantic blocks)

25. [ Alexis AUBRY / Mario LEZOCHE] 25
• A three-phases procedure to
build the semantic blocks
i. 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.
�1
�2
�3
�5
�8
�4 �7
�6
C1
C2
C3
C4
1..*
C1C2
*
1C1C3
*
1..*
C3C4
0..1
C5
1..*C2C5_20..1
1 C2C5_1 *
C8
1
C5C8
*
C6
1..*
C3C6
1..*
C71C4C7_21
1..*
C6C7
*
1
C6C80..1
*
C5C6
0..1
*
C2C3
*
1
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1..*
0..1
C1C8
0..1
*
C8C8
1
𝑆��1
𝑆��2
𝑆��3
𝑆��4
Semantics Structure enactment
(Semantic blocks)

26. [ Alexis AUBRY / Mario LEZOCHE]
Future works
26
IS2
Product
We ight-un We ight-va l
0..1
Has
1
0..1
Has
1
CRM IS1
IS2
IS
APS
Conceptual
Model 2
Conceptual
Model 1
Conceptualisation Fact oriented
modelling
Fact-oriented
Conceptual
Model 1
Fact-oriented
Conceptual
Model 2
PR OD UCT
+Weight-val[0..1]:int
+Weight-un[0..1]:UNIT
IS1
PROD UCT WEIGHT
+val[1]:int
+un[1]:UNIT
0..11
PRODUCT WEIGHT
va l un
1
Has 1 1 Has
1
1 0..1
Semantics structure
enactment
Non-interoperation
Semantic
Blocks of
Model 1
Semantic
Blocks of
Model 2
Semantic gap
evaluation
(Yahia, 2011)
Semantic gap
¬ IS1 ⊓ IS2
Common semantics
IS1⊓ IS2
Semantic gap
IS1 ¬⊓ IS2
Semantic gap
IS1 ⊔ IS2

27. [ Alexis AUBRY / Mario LEZOCHE]
Enterprise Information Systems: a proposition for
conceptualisation and interoperability evaluation
Alexis AUBRY
Mario LEZOCHE
Thank you for your attention !
2010, 10th
November