Building a high-level architecture federated interoperable framework from legacy information systems

International audienceThis paper aims at improving the re-implementation of existing information systems when they are called to be involved in a system of systems, i.e. a federation of enterprise information systems that interoperate. The idea is reusing the local experiences coming from the previous development of the existing information system with the process of model discovery. To avoid redeveloping the entire system when the enterprise needs to cooperate with others, this approach proposes to create local interfaces to code and decode information. The interfaces are instantiated by using models discovered. The interfaces are developed in accordance with the high-level architecture (HLA) standard that proposes message interoperability and synchronisation mechanisms among distributed systems. First, this paper recalls the strong points of model-driven architecture (MDA)/architecture-driven modernisation methodologies for model transformation from conceptual level to implementation and the HLA standard. Then, a MDA and HLA framework is proposed to implement distributed enterprise components from the conceptual level through a federated enterprise interoperability approach. In addition, a model reversal methodology is developed under the framework to guide the re-implementation of legacy information systems to achieve desired interoperability with other systems. To extend the scope of the approach, implemented Web services are combined with HLA in order to facilitate the use of HLA in large distributed execution. This paper ends with an implementation example for validating the approach

Unified Reversible Life Cycle for Future Interoperable Enterprise Distributed Information Systems

Best Paper AwardInternational audienceThis paper aims at improving the re-implementation of existing information systems when they are called to be involved in a system of systems, i.e. a federation of enterprise information systems that interoperate. The idea is reusing the local experiences coming from the development of the original information system with the process of Model Discovery and Ontological approach. We give first, a review of ongoing researches on Enterprise Interoperability. The MDA can help to transform concepts and models from the conceptual level to the implementation. The HLA standard, initially designed for military M&S purpose, can be transposed for enterprise interoperability at the implementation level, reusing the years of experiences in distributed systems. From these postulates, we propose a MDA/HLA lifecycle to implement distributed enterprise models from the conceptual level of federated enterprise interoperability approach. In addition to this classical development, we propose a model reversal methodology to help re-implement the legacy information system, in order to achieve the interoperability with other systems

A Harmonized and Reversible Development Framework for HLA-Based Interoperable Application

International audienceEnterprise collaboration is becoming more and more important because of the globalized economic context. The competitiveness of enterprises depends not only on their internal productivity and performance, but also on their ability to collaborate with others. This necessity leads to the development of interoperability, which makes it possible to improve collaborations between enterprises. Therefore, more and more networked enterprises are being developed. Further, enterprise interoperability is one of the most suitable solutions to total enterprise integration

Who Should I Engage with At What Time? A Missing Event Aware Temporal Graph Neural Network

Temporal graph neural network has recently received significant attention due to its wide application scenarios, such as bioinformatics, knowledge graphs, and social networks. There are some temporal graph neural networks that achieve remarkable results. However, these works focus on future event prediction and are performed under the assumption that all historical events are observable. In real-world applications, events are not always observable, and estimating event time is as important as predicting future events. In this paper, we propose MTGN, a missing event-aware temporal graph neural network, which uniformly models evolving graph structure and timing of events to support predicting what will happen in the future and when it will happen.MTGN models the dynamic of both observed and missing events as two coupled temporal point processes, thereby incorporating the effects of missing events into the network. Experimental results on several real-world temporal graphs demonstrate that MTGN significantly outperforms existing methods with up to 89% and 112% more accurate time and link prediction. Code can be found on https://github.com/HIT-ICES/TNNLS-MTGN.Comment: submitted to TNNL