Philippe Boutinaud

Knowledge management and reuse in collaborative product development – a semantic relationship management-based approach

In the current globalised context of industry, constant improvement of competitiveness remains a major issue for companies. Through the past few years, considering the latest technological advances, product complexity, as well as related manufacturing processes has increased. Nowadays, product lifecycle management systems are seen to be essential and strategic components of enterprise information system in order to harmonise collaborative data management especially in the collaborative product development. Considering knowledge management and reuse purposes, it is not currently the case when product data distributed in various and heterogeneous information systems must be browsed. In such a context, methods and tools for information and knowledge extraction (from heterogeneous systems) and visualisation play an important role, given their capacity to extract, represent and organise the engineering information and knowledge throughout the whole product lifecycle. The paper proposes a semantic relationship ...

Application of PLM for Bio-Medical Imaging in Neuroscience

Bio-medical imaging (BMI) is currently confronted to similar issues than those of manufacturing industries twenty years ago : the growing amount of data, the heterogeneity and complexity of information coming from diverse disciplines, have to be handled by various actors belonging to different organizations. The researchers of the GIN (Neuroimaging Functional Group) laboratory study brain maps of anatomical and functional cognitive activation of hundred-subject cohorts, acquired with Magnetic Resonance Imaging (MRI). Therefore they want to manage the whole process of their research studies, from raw data to analysis results. Even if some data management systems have been developed to meet the requirements of BMI large-scale research studies, there are still many efforts to do in the integration of all the data and processes along a research study, from raw to refined data. So, the use of the Product Lifecycle Management (PLM) concepts to handle the complexity and characteristics of BMI data is proposed. A PLM neuroimaging datamodel that has been designed in collaboration between the GIN laboratory, Roberval laboratory and Cadesis company to meet the needs of the GIN, is described.

Semantic Relationship Based Knowledge Management and Reuse in Collaborative Product Development

Latest technological advances, driven by an increased industrial competitiveness, lead to the development of more complex technical products, which implement technologies from multiple fields of expertise (mechanics, electronics…). The successful design of such kind of products requires the ability of the involved team to communicate, collaborate and integrate their knowledge and know how. This usually causes data integrity problems, making it more difficult to access product information and knowledge distributed in various and heterogeneous systems, for knowledge management and reuse purposes. In such a context of knowledge management, methods and tools for information extraction and visualization play a major role, given their capacity to extract, represent and organize engineering information and knowledge throughout product lifecycle. The paper proposes a semantic relationship based management approach to improve product Beginning Of Life (BOL).

PLMXQuery: Towards a Standard PLM Querying Approach

Experience with data exchange standards has shown considerable limitations in their integration in commercial tools and their interoperability. The current trend is to use XML as a mean of exchanging data. We present in this paper a new standard approach for querying and exporting data from PLM in XML format using XPath/XQuery languages. An abstraction effort and an adapter implementation were required to make PLM content as usable as XML document. Afterward, the resulting XML content could be directly reused or converted to another format.

Ontology–based approach for product information exchange

The paper presents smooth product information and knowledge exchange (SPIKE), an ontology–based methodology for exchanging information between product lifecycle management (PLM) systems. SPIKE does not concern a limited manufacturing field or specific industrial sector. The concerned information deals with the whole product lifecycle information except the geometric or 3D data. The product lifecycle information is not handled exclusively by PLM systems; they are also managed by other systems such as enterprise resource planning. The proposed exchange approach does not lead to any technology constraint or tool. It presents an implementation of SPIKE based on the standard semantic web languages via the I–Semantec platform.

Towards an Enhancement of Relationships Browsing in Mature PLM Systems

Product Lifecycle Management (PLM) domain is at a key point in its development: its concepts and technologies are mature. PLM systems not only manage documents but information associated to a product along its lifecycle, such as Bills-Of-Material (BOM) or require- ments at different levels of granularity. All the dependencies between concepts lead to complex relationships from which it is not easy to get a coherent overview. The purpose of the paper is to know whether PLM systems are able to deal relationships complexity. Two case studies – one form manufacturing industry and the other one from a new application domain of PLM, Bio-Medical Imaging - are developed in the paper. They show that hierarchical browsing of existing PLM systems is not suitable to manage relationships complexity and must evolve to graph browsing.

PLM as a strategy for the management of heterogeneous information in bio-medical imaging field

The amount of heterogeneous information that researchers in bio-medical imaging BMI field have to manage has grown significantly, and their costs remain high. Large-scale sharing and reusing of this information has become unavoidable. Some data management systems have been developed in neuroimaging field, however they miss to integrate the data provenance all along the research works, from study specifications to scientific publication. The manufacturing industry was confronted to similar issues twenty years ago and designed product lifecycle management PLM systems to properly share and manage product information all along its lifecycle and among project teams. Therefore, PLM systems are proposed to be a relevant strategy to manage BMI research studies information. The generic, flexible and PLM-oriented data model called BMI-lifecycle management BMI-LM is described, as well as a neuroimaging classification which brings flexibility to the information management system. A test implementation into a PLM system is presented, and the feedback from the GIN researchers is discussed.

BIOMIST: A Platform for Biomedical Data Lifecycle Management of Neuroimaging Cohorts

The data management needs of the neuroimaging community are currently addressed by several specialized software platforms, which automate repetitive data import, archiving and processing tasks. The BIOMIST (BIOMedical Imaging SemanTic data management) project aims at creating such a framework, yet with a radically different approach: the key insight behind it is the realisation that the data management needs of the neuroimaging community – organizing the secure and convenient storage of large amounts of large files, bringing together data from different scientific domains, managing workflows and access policies, ensuring traceability and sharing data across different labs – are actually strikingly similar to those already expressed by the manufacturing industry. The BIOMIST neuroimaging data management framework is built around the same systems as those that were designed in order to meet the requirements of the industry. Product Lifecycle Management (PLM) systems rely on an object-oriented data model and allow the traceability of data and workflows throughout the life of a product, from its design to its manufacturing, maintenance and end of life, while guaranteeing data consistency and security. The BioMedical Imaging – Lifecycle Management (BMI-LM) data model was designed to handle the specificities of neuroimaging data in PLM systems, throughout the lifecycle of a scientific study. This data model is both flexible and scalable, thanks to the combination of generic objects and domain-specific classes sourced from publicly available ontologies. The Data Integrated Management and Processing (DIMP) method was then designed to handle workflows of processing chains in PLM. Following these principles, workflows are parameterised and launched from the PLM platform onto a computer cluster, and the results automatically return to the PLM where they are archived along with their provenance information. Third, to transform the PLM into a full-fledged neuroimaging framework, we developed a series of external modules: DICOM import, XML form data import web-services, flexible graphical querying interface, and SQL export to spreadsheets. Overall, the BIOMIST platform is well suited for the management of neuroimaging cohorts, and it is currently used for the management of the BIL&GIN dataset (300 participants) and the ongoing MRI-Share cohort acquisition of 2000 participants.

Using Ontologies to Access Complex Data: Applications on Bio-Imaging

Information Systems, used to share information, lead to the growth of heterogeneous data and then the dependencies between them. Thus, the links and dependencies among heterogeneous and distributed data are more and more complex during daily activities of users (researchers, engineers, etc.). Our contribution is to propose a methodology to facilitate the exploitation (interrogation and sharing) of complex data in an organization. The system, we propose, tends to mix semantic approach with data management.

Processing and Visual Analyze of Heterogeneous and Multidimensional Data in Biomedical PLM Context

The emergence of PLM for biomedical imaging lifecycle management highlights the needs for management and analysis of heterogeneous, complex and multidimensional data in PLM systems. Data provenance in biomedical imaging domain is complex, notably provenance of processing data, and to ensure full traceability in a purpose of reuse, processing operations must be integrated to PLM systems and processing provenance must be easily analyzable by users. The DIMP (Data Integrated Management and Processing) method was designed for this objective: it allows user to launch easily processing chains from PLM systems and ensures a full management of provenance. The MDG (Multidimensional Dynamic Graph) representation is introduced to formalize complex provenance and data relationships. JGEX (Json Graph EXchange) file format and NeuroGraphViewer web graph visualization client have been developed to facilitate the analysis of MDG. An application of the DIMP method to the study of functional brain connectivity through MDG analysis encourages further work on analysis of complex relationships in PLM systems.