Machine learning approaches propose a promising solution for extracting and analyzing contextual information from different data sources. Especially, in the E-health field, the need for Improvement in risk prediction, early detection and prevention of disease remains an essential task for the well-being of patients at risk. In this paper, we aim to explore the added value of using machine learning based approach to predict contextual situations in context-aware systems, and more specifically the field of E-health.

Design of complex systems goes through a multiview paradigm in which separate teams, from different business viewpoints, build partial models describing the system. As they are expressed in different languages, these partial models are called heterogeneous models. To maintain the global system's consistency, we propose a collaborative approach that combines Group Decision Making (GDM) and Model-Based Engineering. This paper presents a metamodel for collaborative decision elaboration via a set of decision policies which are instances of GDM patterns. Our approach is illustrated with a hospital Emergency Department case study and is supported by a tool allowing models alignment through GDM based processes.

Design of complex systems implies various points of view expressed by stakeholders with different areas of expertise. Each stakeholder describes his model in a Domain Specific Language, according to his point of view. Ensuring the consistency of the global system and building a cross view is a challenging task. It requires the involvement of all stakeholders to produce intermodel correspondences that satisfy their concerns. In this paper, we first introduce a metamodel of collaboration that formalizes collaborative work, then we use this metamodel to define a collaborative process for heterogeneous design models matching. This approach establishes semantic links at metamodel level by following a group decision-making process, then it refines those links semi-automatically at model level by exploiting their semantics.

This paper falls into the context of modeling complex systems according to various viewpoints. More precisely, it presents an iterative process of heterogeneous models consistency management - by taking into account various types of evolution - based on building a correspondence model. In the case of models evolution, this process is intended to capture changes in the models, to list modifications to be made in the impacted models and finally to update the correspondence model for a future iteration.

This work is situated in the context of the application of Model Driven Engineering to complex systems view-based modelling. In fact, view-based models - called also partial models - are manipulated by different actors (designers), and are thus generally heterogeneous, that is, described with different DSLs (Domain Specific Languages). Instead of building a single global model, which is not realistic, we propose to organize the different partial models as a network of related models, which provides a global view of the system through a correspondence model. As models are modelled separately by different designers, they also evolve separately that induces a problem of consistency. To solve it, we propose a semi-automatic process based on the correspondence model allowing detecting changes, calculating their impacts, and proposing modifications to maintain the consistency among them. The approach is supported by a tool chain and illustrated by the example of a Bug Tracking System.

Several approaches adopted by the software engineering community rely on the principle of multi-modeling which allows to separate concerns and to model a system as a set of less complex sub-models. Model composition is a crucial activity in Model Driven Engineering (MDE). It is particularly useful when adopting a multi-modeling approach to analyze and design software systems. In previous work, we have defined a view-based UML profile called VUML. In this paper, we describe a formal approach for model composition in which we consider the composition as an algebraic operator on the set of UML-compliant models. We specify the semantics of our composition operator by means of graph transformations. Furthermore, we present a composition scheme interpreted by a two-steps composition process based on two strategies of correspondence and merging. To illustrate our approach, we apply it to the composition of UML class models diagrams into one VUML model class diagram.

VUML is a view-based analysis/design method offering a formalism (UML profile) and a process to model software systems through objects and views. The main extension to UML is the multiview class whose goal is to store and deliver information according to the user's viewpoint. VUML supports the dynamic change of viewpoints and offers mechanisms to describe view dependencies. On the semantics side, the VUML meta-model extends the UML one with OCL rules. VUML proposes also a generic implementation pattern to generate the object code corresponding to a VUML class diagram. The proposed pattern uses delegation, handle technique and polymorphism to implement views. In this paper, we present a model driven implementation of this pattern, which integrates the multi-user aspect and supports Java language. We propose a code generation technique that respects the pattern and uses the MDA approach. We specify transformations as a collection of rules implemented in ATL. The proposal is illustrated by a simple example.

Model composition is considered as a crucial activity in model driven software development (MDSD). It is a common feature when adopting a multi-modeling approach to analyze and design software systems. Similar modular approaches are known under several names such as viewpoints, aspects, subjects, etc. In previous works, we proposed a view-based UML profile called VUML. In this paper, we describe a conceptual framework whose goal is to separate the generic composition part from the specific part dedicated to a given modeling domain. We apply our approach to the composition of UML class diagrams into one VUML class diagram.