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Alexis AUBRY, Mario LEZOCHE. Enterprise Information Systems: a proposition for conceptualisation and interoperability evaluation

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Presentation from the 1st Workshop on Future Internet Enterprise Systems - FINES 2010: Ontologies and Interoperability, made at 10.11.2010 in Faculty of Mechanical Engineering, Laboratory for …

Presentation from the 1st Workshop on Future Internet Enterprise Systems - FINES 2010: Ontologies and Interoperability, made at 10.11.2010 in Faculty of Mechanical Engineering, Laboratory for Intelligent Manufacturing Systems

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  • In the product-centric paradigm we assume that the product is a particular information system
    In fact new technologies as RFID could allow it.
    The product has to contain all the information concerning its fabrication along its life-cycle.
    the different ISs cooperate with this product to ensure the mission of the enterprise.
  • Organisational barriers are still an important issue but out of scope of our skills.
    The technological barriers are strongly studied by researchers in computer science: one solution direction is the models transformation
    We are interesting only by the semantic barriers by studying the semantic interoperability and studying the semantic loss during one exchange of information
  • Considering the extract of the two conceptual models. Intuitively, those classes carry the same semantics.
    The first representation facilitates querying for specific values related to the weight for example.
  • Fact-oriented modelling is a conceptual, natural language based approach that facilitates the mapping between conceptual representations.
    Object role modelling is the most popular fact oriented approach. ORM makes no explicit use of attributes.
    Instantiating ORM with UML class diagram, induces extracting the attributes and modelling it as classes.
    That permits to represent the semantics at the same level.
    One post-doc is working with CRAN to search how the fact-oriented modelling can be used to facilitate the semantic mapping between two Iss.
    We make now the assumption that this fact-oriented model is available for each studied IS.
  • To facilitate the evaluation of the semantic gap between two ISs, the goal of semantic enactement is to highlight the semantics of each concept that is part of the conceptual model and then aiding the mapping with the concepts of the other IS.
    To do so we propose the concept of semantic block associated with one concept.
    This semantic block will containt all the concepts that are mandatory for representing the minimal semantics of the concept.
  • Flexnet is a MES (Manufacturing Execution System) IT systems that manage production in factories.
    Adonix Sage is an ERP (Enterprise Resource Planning) a system that is used to manage and coordinate all the resources, information, and functions of a business.
    It’s not possible to use an automatic system to recreate relations through names because there are a lot of duplications.
    But it’s possible to automatically aid the expert underlying
  • Rules presented before can be implemented in Mega application
  • Let us consider the concept C2.
    As mentioned by the associate C2C5_1, a given instance of this concept exists if it is associated with at least one instance of the concept C5. That means that C5 is mandatory for expressing the semantics of C2.
    Moreover an instance of C5 exists if its associated with exactly one instance of C8.
    On the contrary, as the minimal multiplicity is 0 for the role of C1 when considering the association C1C2, the existence of any instance of C2 is not conditionned by the existence of one instance of C1.
    Continuing the same reasoning, step by step, all the concepts necessary for the semantics definition of C2 can be highlighted.
  • To facilitate the building of these semantic blocks, we propose to associate with the conceptual model, a directed graph denoted as semantic-relationships graph.
    In this graph, each node is associated with a concept.
    The edge (Ci,Cj) exists if
    There is an association between Ci and Cj in the conceptual model
    The minimal multiplicity of the role of Cj for this association is equal to 1
    The existence of the edge represents the fact that Cj is mandatory for expressing the semantics of Ci.
  • We have proposed an algorithm for realising the the third step of this procedure.
    This algorithm is based on a depth first search approach on the graph of the strongly connected components and remains polynomial.
    Moreover this algorithm uses the different properties presented earlier to build rapidly the semantic blocks
    The whole procedure has been implemented in the MEGA EA suite that provides repository-based modelling tools and supports UML diagrams.
    Moreover MEGA EA suite supports VB macro.
  • Applying the algorithm to a real information system (Flexnet, Sage X3)
    For example Sage x3 induces 1200 concepts and 1000 associations
    See how to use the semantic blocks to highlight and evaluate the semantic gap ?
    How to use these results to measure the effects of these gaps on the non-interoperation.
    It’s the goal of the Ph.D thesis of Esma Yahia who should defend at the end of 2011
  • Transcript

    • 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 C4C7_1 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