Système sémantique pour la customisation dynamique des modèles d'information de la PLM

Pas de résumé en françaisWe live in the information age. Data has become an essential asset for most everyday situations and business interactions. The need to share data, to generate information, and create new knowledge from that data is common to all fields of research and all economic activity. Managing data is a critical, and sometimes costly, process. When not properly defined, data might become incomplete, inconsistent or, even worse, unusable. Requirements for data evolve and we must define new data or update existing data over the entire data lifecycle. Evolving data requirements is an important issue and a technological challenge as it is not possible to define, in advance, information structures that meet requirements you do not yet know. Specifying information requirements is particularly challenging in domains such as manufacturing where information exchange involves many actors and sharing across multiple functions and software applications. As a result, it becomes hard to find a common information structure for representing data. The challenge is even bigger when a temporal aspect has to be considered since it requires the ability to extend the information structure dynamically over time. One area within the manufacturing domain that we have identified with these characteristics is Product Lifecycle Management (PLM). PLM involves many global actors using a myriad of software applications that perform a series of product management functions that can last from weeks to decades. Because the mechanism to extend models is static by its nature, requiring numerous updates of the initial information model, this operation is expensive in cost and time, and requires and understanding of the entire initial model to ensure correct extensions are developed. This research presents an alternative based on dynamic customization of information models in the context of PLM, by leveraging existing PLM standards and frameworks, and using emerging semantic web technologies such as OWL, SPARQL and SPIN. Following a state of the art in Chapter 2, Chapter 3 defines technical requirements used to evaluate existing PLM standards and frameworks. Based on the analysis of this evaluation, Chapter 4 presents new framework components for defining dynamically customizable information models for PLM. In chapter 5 these components are integrated together into a framework, and a use case demonstrates the efficiency of the framework. Chapter 6 concludes the research and introduces ideas for future research