Handling domain knowledge in system design models. An ontology based approach.

Complex systems models are designed in heterogeneous domains and this heterogeneity is rarely considered explicitly when describing and validating processes. Moreover, these systems usually involve several domain experts and several design models corresponding to different analyses (views) of the same system. However, no explicit information regarding the characteristics neither of the domain nor of the performed system analyses is given. In our thesis, we propose a general framework offering first, the formalization of domain knowledge using ontologies and second, the capability to strengthen design models by making explicit references to the domain knowledgeformalized in these ontology. This framework also provides resources for making explicit the features of an analysis by formalizing them within models qualified as ‘’points of view ‘’. We have set up two deployments of our approach: a Model Driven Engineering (MDE) based deployment and a formal methods one based on proof and refinement. This general framework has been validated on several no trivial case studies issued from system engineering

Explicitation de la sémantique du domaine dans les modèles de systèmes : une approche à base d'ontologies

Les modèles de systèmes complexes sont conçus dans différents contextes. Cependant, l'hétérogénéité induite par ces contextes n’est pas prise en compte lors de la description et de la validation de ces systèmes. De plus, ces systèmes impliquent généralement l’intervention deplusieurs experts du domaine et la réalisation de plusieurs modèles correspondant à différentes analyses (vues) de ce même système. Aucune information concernant les caractéristiques du domaine ni des analyses réalisées n'est explicitée. Nous proposons un cadre méthodologiquepermettant d’une part, de formaliser les connaissances de domaine à l’aide d’ontologies, et d’autre part d'enrichir les modèles à l’aide des connaissances de domaine en définissant des références explicites aux informations formalisées dans ces ontologies. Ce cadre permet également de rendre explicites les caractéristiques d'une analyse en les formalisant dans des modèles qualifiés de «points de vue». Nous avons réalisé deux déploiements de ce cadre méthodologique : un premier déploiement utilisant les techniques de l’Ingénierie Dirigée par les Modèles (IDM) et un second fondé sur les méthodes formelles basées sur des techniques de preuve et de raffinement. Ce cadre a été validé sur plusieurs cas d'études non triviaux issus de l'ingénierie système.Complex systems models are designed in heterogeneous domains and this heterogeneity is rarely considered explicitly when describing and validating processes. Moreover, these systems usually involve several domain experts and several design models corresponding to different analyses (views) of the same system. However, no explicit information regarding the characteristics neither of the domain nor of the performed system analyses is given. In our thesis, we propose a general framework offering first, the formalization of domain knowledge using ontologies and second, the capability to strengthen design models by making explicit references to the domain knowledgeformalized in these ontology. This framework also provides resources for making explicit the features of an analysis by formalizing them within models qualified as ‘’points of view ‘’. We have set up two deployments of our approach: a Model Driven Engineering (MDE) based deployment and a formal methods one based on proof and refinement. This general framework has been validated on several no trivial case studies issued from system engineering

Annotation of Engineering Models by References to Domain Ontologies

International audienceComplex engineering systems execute within different contexts and domains. The heterogeneity induced by these contexts is usually implicitly handled in the development cycle of such systems. We claim that reducing this heterogeneity can be achieved by handling explicitly the knowledge mined from these domains and contexts. Verification and validation activities are improved due to the expression and verification of new constraints and properties directly extracted from the context and domains associated to the models. In this paper, we advocate the use of domain ontologies to express both domain and context knowledge. We propose to enrich design models that describe complex information systems, with domain knowledge, expressed by ontologies, provided by their context of use. This enrichment is achieved by annotation of the design models by references to ontologies. Three annotation mechanisms are proposed. The resulting annotated models are checked to validate the new minded domain properties. We have experimented this approach in a model driven engineering (MDE) development setting

Strengthening MDE and Formal Design Models by References to Domain Ontologies. A Model Annotation Based Approach

International audienceCritical systems are running in heterogeneous domains. This heterogeneity is rarely considered explicitly when describing and validating processes. Handling explicitly such domain knowledge increases design models robustness due to the expression and validation of new properties mined from the domain models. This paper proposes a stepwise approach to enrich design models describing complex information systems with domain knowledge. We use ontologies to model such domain knowledge. Design models are annotated by references to domain ontologies. The resulting annotated models are checked. It becomes possible to verify domain-related properties and obtain strengthened models. The approach is deployed for two design model development approaches: a Model Driven Engineering (MDE) approach and a correct by construction formal modeling one based on refinement and proof using Event-B method. A case study illustrates both approaches (This work is partially supported by the French ANR-IMPEX project.)

Quality-Based Reinforcement Learning in Intelligent Opportunistic Software Composition

International audienceInternet of Things and cyber-physical systems are characterised by openness and an increasing number of devices and their associated services. In a previous work, we have proposed to exploit opportunistically these services in order to automatically make emerge customised applications that suit user preferences.For that, we have developed a generic solution for bottom-up opportunistic service composition, based on reinforcement learning.In this work, it is extended to handle more efficiently the appearance of new components using \textit{service annotation} and \textit{quality attributes} in order to generalise and share knowledge with new discovered services. A didactic use case is used for illustration and demonstration purposes

Formal Modelling of Ontologies : An Event-B based Approach Using the Rodin Platform

International audienceThis paper reports on the results of the French ANR IMPEX research project dealing with making ex- plicit domain knowledge in design models. Ontologies are formalised as theories with sets, axioms, theorems and reasoning rules. They are integrated to design models through an annotation mecha- nism. Event-B has been chosen as the ground formal modelling technique for all our developments. In this paper, we particularly describe how ontologies are formalised as Event-B theories

Formal modelling of ontologies within Event-B

International audienceThis paper reports on the results of the French ANR IMPEX research project dealing with making explicit domain knowledge in design models. Ontologies are formalised as theories with sets, axioms, theorems and reasoning rules. They are integrated to design models through an annotation mechanism. Event-B has been chosen as the ground formal modelling technique for all our developments. In this paper, we particularly describe how ontologies are formalised as Event-B theories

Formal modelling of ontologies within Event-B

International audienceThis paper reports on the results of the French ANR IMPEX research project dealing with making explicit domain knowledge in design models. Ontologies are formalised as theories with sets, axioms, theorems and reasoning rules. They are integrated to design models through an annotation mechanism. Event-B has been chosen as the ground formal modelling technique for all our developments. In this paper, we particularly describe how ontologies are formalised as Event-B theories

Automatic and Intelligent Composition of Pervasive Applications - Demonstration

International audienceOpportunistic service composition is a novel and disruptive approach for building software in dynamic and open pervasive environments. It aims to tackle the growing complexity of software design in such environments by dynamically providing relevant applications in the absence of explicit user needs: an intelligent engine composes software components that are present in the pervasive environment in order to build usertailored context-adapted applications, relying on reinforcement learning. This demonstration presents the current status of our opportunistic composition prototype: the intelligent engine builds pervasive applications in bottom-up mode from actual software components that are discovered via the UPnP (Universal Plug and Play) protocol, taking into account learned user preferences