On-demand data integration is among the key challenges in Cloud Computing. In this paper, we present an ontology-based framework for describing and integrating data on the fly to answer transient business needs. We provide a semantic modeling for cloud's data services. The proposed modeling makes it possible to automatically resolve the different types of data heterogeneity that would arise when data from heterogeneous and autonomous providers need to be combined together to answer the business's data needs.

Today, in order to confront the growing complexity of products and organisations, Aeronautic, Space and Defence (ASD) manufacturing enterprises are using more and more System Engineering, Product Lifecycle Management and Computer Aided Solutions for various engineering and management activities. Combined with a more and more important outsourcing, such trends led to the emergence of what is called Dynamic Manufacturing Networks (DMN). These DMNs are facing important difficulties for the establishment of PLM interoperability based on legacy PLM standards for Manufactured Product and Process data exchange, sharing and long term archiving. To address such issues, Airbus Group Innovations (AGI) has been developing a Federative Interoperability Framework (FIF) through iterations between research, operational and standardisation projects. FIF defines interoperability principles, brakes and enablers. Based on an analysis of DMN interoperability brakes and enablers, this paper proposes a new way based on FIF interoperability principles for dealing with pragmatic interoperability of PLM processes within the ASD digital business ecosystem. We propose a DMN interoperability conceptual framework, coupled with an experimental collaborative open platform (cPlatform), to achieve pragmatic PLM process interoperability. For this, we rely on DMN blueprint of PLM Business Processes developed within the frame of the IMAGINE project. The proposed approach is then assessed according to scientific, business and standardisation viewpoints.

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We propose a service-oriented privacy-preserving model for data integration across autonomous clouds. Our model allows to execute aggregations of cloud data sharing services without revealing any extra information to any of the involved services, i.e., none of involved services nor their providers are able to learn/infer any information about the data the other services provide beyond what these services already know.