A Framework for Natural Enterprise Modelling

International audienceWithin enterprise modelling, models are typically needed for a range of different purposes, ranging from vision and strategy development to computer-aided analyses. It is well known that model's content and form need to be adapted to its purpose. This typically concerns the tuning in terms of granularity, visualisation, precision and formality of the model, as well as in terms of the concepts/language in which the model is expressed. However, typical modelling tools lack such support. A number of empirical observations points at a lack in flexibility of tools and underlying modelling languages to aptly fit the needs of specific modelling situations. For instance, it is observed that fixed metamodels make it difficult to align the language with e.g. organisation-specific domains/concerns. This often leads to the different levels of discipline in which a fixed modelling language is obeyed to, or even the use of home-grown notations instead of fixed standard ones. Likewise, to compensate the lack of flexibility in dedicated modelling tools, classical drawing tools or paper are used as modelling support. Once models created this way transition to the more formal tasks, a lot of redundant work and increased effort is needed to ensure consistency and coherence among different enterprise models. As a result of an ongoing research, this paper discusses the need to adapt the models and modelling environments to specific modelling situations. In particular, we explore the concept of natural enterprise modelling, as a strategy for enabling the flexibility while also ensuring the coherence in modelling. We also sketch potential high level design of a flexible modelling infrastructure supporting natural enterprise modelling, and indicate some promising future research directions

Generation of Multi-Device Adaptive MultiModal Web Applications

Abstract. This paper presents a set of tools to support multimodal adaptive Web applications. The contributions include a novel solution for generating multimodal interactive applications, which can be executed in any browserenabled device; and run-time support for obtaining multimodal adaptations at various granularity levels, which can be specified through a language for adaptation rules. The architecture is able to exploit model-based user interface descriptions and adaptation rules in order to achieve adaptive behaviour that can be triggered by dynamic changes in the context of use. We also report on an example application and a user test concerning adaptation rules changing dynamically its multimodality

Fuzzy4U : un système en logique floue pour l’adaptation des interfaces utilisateur

International audienceUser Interfaces adaptation is a well-known requirement in human-computer interaction. However, even if many works address this topic, some challenges such as context uncertainty and a combination of adaptation rules, are still remaining. This article tackles these challenges by using fuzzy logic to manage adaptation. It proposes an architecture where an adaptation engine is supported by fuzzy logic. It shows its benefits and compares it with an approach using crisp logic.L’adaptation des interfaces est un besoin reconnu en interaction homme-machine. Cependant, même si de nombreux travaux ont abordé cette thématique, il demeure des verrous tels que l’incertitude du contexte d’usage et de la combinaison des règles d’adaptation. Cet article aborde ces verrous en proposant d’utiliser la logique floue pour gérer l’adaptation. Il propose une architecture dans laquelle le moteur d’adaptation s’appuie sur la logique floue. Il en montre les bénéfices et compare l’approche avec une approche en logique booléenne

Ambient Intelligence Users in the Loop: Towards a Model-Driven Approach

International audienceAmbient and mobile systems consist of networked devices and software components surrounding human users and providing services. From the services present in the environment, other services can be composed opportunistically and automatically by an intelligent system and proposed to the user. The latter must not only to be aware of existing services but also be kept in the loop in order to both control actively the services and influence the automated decisions. This paper first explores the requirements for placing the user in the ambient intelligence loop. Then it describes our approach aimed at answering the requirements, which originality sets in the use of the model-driven engineering paradigm. It reports on the prototype that has been developed , and analyzes the current status of our work towards the different research questions that we have identified

Induction of PCFT and OATP1A2 via vitamin D receptor activation in vitro is not confirmed in vivo in healthy volunteers after a 10-days treatment with 1,25-dihydroxyvitamin D3

A self-adaptive software system modifies its behavior at runtime in response to changes within the system or in its execution environment. The fulfillment of the system requirements needs to be guaranteed even in the presence of adverse conditions and adaptations. Thus, a key challenge for self-adaptive software systems is assurance. Traditionally, confidence in the correctness of a system is gained through a variety of activities and processes performed at development time, such as design analysis and testing. In the presence of self-adaptation, however, some of the assurance tasks may need to be performed at runtime. This need calls for the development of techniques that enable continuous assurance throughout the software life cycle. Fundamental to the development of runtime assurance techniques is research into the use of models at runtime (M@RT). This chapter explores the state of the art for using M@RT to address the assurance of self-adaptive software systems. It defines what information can be captured by M@RT, specifically for the purpose of assurance, and puts this definition into the context of existing work. We then outline key research challenges for assurance at runtime and characterize assurance methods. The chapter concludes with an exploration of selected application areas where M@RT could provide significant benefits beyond existing assurance techniques for adaptive systems