GRAI-ICE Model Driven Interoperability Architecture for Developing Interoperable ESA

International audienceThis paper presents GRAI-ICE Model Driven Interoperability Architecture (MDI) which is developed based on MDA (Model Driven Architecture) of OMG and some initial works performed in INTEROP NoE. This MDI architecture aims at supporting the development of changeable on-demand and interoperable ESA (Enterprise Software Application). The architecture defined five modelling levels, i.e., Top CIM, Bottom CIM, Object oriented PIM, Pattern oriented PSM, and Component and configuration oriented CODE. This paper presents in detail core concepts and rational of each modeling level. An application example in nuclear equipment industry is outlined

Intelligent negotiation mechanism for supporting the interoperability within the sensing enterprise

The authors wish to acknowledge the support of the European Commission through the funding of the UNITE, MSEE and IMAGINE FP7 projects, and the European Space Agency - Concurrent Design Facility (ESA-CDF) for their support in the development of the business case presented in this paper.The Sensing Enterprise is a novel concept that refers to an enterprise anticipating future decisions by using multi-dimensional information captured through physical and virtual objects. The Sensing Enterprise concept is shifting focus towards a borderless enterprise, having at its core the collaboration and continuous interactions among smart objects and systems. But in the actual competitive and global business context, the maintenance of the collaboration environment through the interoperation among heterogeneous smart virtual and physical objects in a collaborative organizational environment becomes difficult to achieve. Therefore, in a dynamic context a change in any component of the networked partners affects the others, creating difficulties to sustain operating networked environment. In this respect, this paper proposes an intelligent negotiation framework as a key mechanism to achieve and maintain the interoperability between the organisations' smart objects and applications, and its validation in an industrial scenario. To allow a sustainable, flexible and generic approach towards the infrastructure implementation in global scale, a cloud-based platform is proposed for setting of the Sensing Enterprise framework.publishersversionpublishe

NEGOSEIO: framework for the sustainability of model-oriented enterprise interoperability

Dissertation to obtain the degree of Doctor of Philosophy in Electrical and Computer Engineering(Industrial Information Systems)This dissertation tackles the problematic of Enterprise Interoperability in the current globally connected world. The evolution of the Information and Communication Technologies has endorsed the establishment of fast, secure and robust data exchanges, promoting the development of networked solutions. This allowed the specialisation of enterprises (particularly SMEs) and favoured the development of complex and heterogeneous provider systems. Enterprises are abandoning their self-centrism and working together on the development of more complete solutions. Entire business solutions are built integrating several enterprises (e.g., in supply chains, enterprise nesting) towards a common objective. Additionally, technologies, platforms, trends, standards and regulations keep evolving and demanding enterprises compliance. This evolution needs to be continuous, and is naturally followed by a constant update of each networked enterprise’s interfaces, assets, methods and processes. This unstable environment of perpetual change is causing major concerns in both SMEs and customers as the current interoperability grounds are frail, easily leading to periods of downtime, where business is not possible. The pressure to restore interoperability rapidly often leads to patching and to the adoption of immature solutions, contributing to deteriorate even more the interoperable environment. This dissertation proposes the adoption of NEGOSEIO, a framework that tackles interoperability issues by developing strong model-based knowledge assets and promoting continuous improvement and adaptation for increasing the sustainability of interoperability on enterprise systems. It presents the research motivations and the developed framework’s main blocks, which include model-based knowledge management, collaboration service-oriented architectures implemented over a cloud-based solution, and focusing particularly on its negotiation core mechanism to handle inconsistencies and solutions for the detected interoperability problems. It concludes by validating the research and the proposed framework, presenting its application in a real business case of aerospace mission design on the European Space Agency (ESA).FP7 ENSEMBLE, UNITE, MSEE and IMAGINE project

Monitoring and Information Alignment in Pursuit of an IoT-Enabled Self-Sustainable Interoperability

To remain competitive with big corporations, small and medium-sized enterprises (SMEs) often need to be more dynamic, adapt to new business situations, react faster, and thereby survive in today‘s global economy. To do so, SMEs normally seek to create consortiums, thus gaining access to new and more opportunities. However, this strategy may also lead to complications. Due to the different sources of enterprise models and semantics, organizations are experiencing difficulties in seamlessly exchanging vital information via electronic means. In their attempt to address this issue, most seek to achieve interoperability by establishing peer-to-peer mappings with different business partners, or by using neutral data standards to regulate communications in optimized networks. Moreover, systems are more and more dynamic, frequently changing to answer new customer‘s requirements, causing new interoperability problems and a reduction of efficiency. Another situation that is constantly changing is the devices used in the enterprises, as the Enterprise Information Systems, devices are used to register internal data, and to be used to monitor several aspects. These devices are constantly changing, following the evolution and growth of the market. So, it is important to monitor these devices and doing a model representation of them. This dissertation proposes a self-sustainable interoperable framework to monitor existing enterprise information systems and their devices, monitor the device/enterprise network for changes and automatically detecting model changes. With this, network harmonization disruptions are detected in a timely way, and possible solutions are suggested to regain the interoperable status, thus enhancing robustness for reaching sustainability of business networks along time

Semantic discovery and reuse of business process patterns

Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modelling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. As a statement of research-in-progress this paper focuses on business process patterns and proposes an initial methodological framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework borrows ideas from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse

Bioinspired metaheuristic algorithms for global optimization

This paper presents concise comparison study of newly developed bioinspired algorithms for global optimization problems. Three different metaheuristic techniques, namely Accelerated Particle Swarm Optimization (APSO), Firefly Algorithm (FA), and Grey Wolf Optimizer (GWO) are investigated and implemented in Matlab environment. These methods are compared on four unimodal and multimodal nonlinear functions in order to find global optimum values. Computational results indicate that GWO outperforms other intelligent techniques, and that all aforementioned algorithms can be successfully used for optimization of continuous functions

Experimental Evaluation of Growing and Pruning Hyper Basis Function Neural Networks Trained with Extended Information Filter

In this paper we test Extended Information Filter (EIF) for sequential training of Hyper Basis Function Neural Networks with growing and pruning ability (HBF-GP). The HBF neuron allows different scaling of input dimensions to provide better generalization property when dealing with complex nonlinear problems in engineering practice. The main intuition behind HBF is in generalization of Gaussian type of neuron that applies Mahalanobis-like distance as a distance metrics between input training sample and prototype vector. We exploit concept of neuron’s significance and allow growing and pruning of HBF neurons during sequential learning process. From engineer’s perspective, EIF is attractive for training of neural networks because it allows a designer to have scarce initial knowledge of the system/problem. Extensive experimental study shows that HBF neural network trained with EIF achieves same prediction error and compactness of network topology when compared to EKF, but without the need to know initial state uncertainty, which is its main advantage over EKF

Metodología de implantación de modelos de gestión de la información dentro de los sistemas de planificación de recursos empresariales. Aplicación en la pequeña y mediana empresa

La Siguiente Generación de Sistemas de Fabricación (SGSF) trata de dar respuesta a los requerimientos de los nuevos modelos de empresas, en contextos de inteligencia, agilidad y adaptabilidad en un entono global y virtual. La Planificación de Recursos Empresariales (ERP) con soportes de gestión del producto (PDM) y el ciclo de vida del producto (PLM) proporciona soluciones de gestión empresarial sobre la base de un uso coherente de tecnologías de la información para la implantación en sistemas CIM (Computer-Integrated Manufacturing), con un alto grado de adaptabilidad a la estnictura organizativa deseada. En general, esta implementación se lleva desarrollando hace tiempo en grandes empresas, siendo menor (casi nula) su extensión a PYMEs. La presente Tesis Doctoral, define y desarrolla una nueva metodología de implementación pan la generación automática de la información en los procesos de negocio que se verifican en empresas con requerimientos adaptados a las necesidades de la SGSF, dentro de los sistemas de gestión de los recursos empresariales (ERP), atendiendo a la influencia del factor humano. La validez del modelo teórico de la metodología mencionada se ha comprobado al implementarlo en una empresa del tipo PYME, del sector de Ingeniería. Para el establecimiento del Estado del Arte de este tema se ha diseñado y aplicado una metodología específica basada en el ciclo de mejora continua de Shewhart/Deming, aplicando las herramientas de búsqueda y análisis bibliográfico disponibles en la red con acceso a las correspondientes bases de datos