Mahmoud El Hamlaoui

Heterogeneous models matching for consistency management

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.

Maintien de la cohérence de modèles de conception hétérogènes

L’application de l’ingenierie dirigee par les modeles au developpement de systemes complexes introduit un ensemble de modeles heterogenes, elabores separement, dans des langages varies. Afin de garantir la bonne execution de l’ensemble et sa coherence, il est necessaire de proceder a la composition des modeles heterogenes, puis a la mise en œuvre d’un ensemble de mecanismes permettant de l’exploiter. Cet article presente un processus semi-automatise qui consiste d’une part a mettre en correspondance des modeles heterogenes, et d’autre part, a maintenir la coherence de l’ensemble par synchronisation des changements. Pour demontrer la faisabilite de l’approche, nous avons developpe un outil support, en l’appliquant a l’exemple d’un systeme de gestions d’anomalies.

A Synergy of Semantic and Context Awareness for Service Composition in Ubiquitous Environment

Service composition in an important facet in service oriented architecture, it’s about the idea of assembling atomic services to satisfy a demand rather than building new applications from ‘scratch’, From the user’s perspective it’s a complex task due to the increasing number of services in the web and their heterogeneity. This complexity is increasing in the internet of Things era wehere computing devices are everywhere. In this work we propose an approach for composition of context aware services in a semantic manner, Artificial Intelligence planning is used to automate the composition starting from a defined objectif containing user request and context parameters. Service are described by extending OWL-S with contextual conditions. The proposed architecture was evaluated through an e-health scenario where chronic patients can benefit from a remote and automated medical supervision and emergency handling.

A MDE Approach for Heterogeneous Models Consistency.

To design a complex system, we often proceed via separation of viewpoints. Each viewpoint is described by a model that represents a domain expertise. Those partial models are generally heterogeneous (i.e conform to different metamodels) and thus performed by different designers. We proposed a matching process that links partial models through a virtual global model in order to create a complete view of the system. As models evolve, we should consider the impact of changing an element involved in a correspondence on other models to keep the coherence of the global view. So, we have defined a process that automatically identify changes, classify them and treat their potential repercussions on elements of other partial models in order to maintain the global model consistency.

Translation of Heterogeneous Requirements Meta-Models Through a Pivot Meta-Model.

Companies use these different approaches to elicit, specify, analyse and validate their requirements in different contexts. The globalization and the rapid development of information technologies sometimes require companies to work together in order to achieve common objectives as quickly as possible. We propose a Unified Requirements Engineering meta-model (UREM) that allows cooperation in the requirements engineering process between heterogeneous RE (Requirement Engineering) models. In this paper, we explore UREM as a pivot meta-model to ensure interoperability between heterogeneous RE models.

Heterogeneous design models alignment: from matching to consistency management

Complex systems involve several business expertises that are designed as models in different modeling languages. These partial models are manipulated by different designers, and are thus generally heterogeneous (i.e conform to different metamodels). To create a complete view of the system, we proposed a process to organize partial models as a network of models through a virtual global model. As models evolve, changing elements involved in a correspondence, may cause the inconsistency of the global model. So, we have defined a process that automatically identify changes, classify them and treat their impacts on elements of other partial models in order to maintain the global model consistency.

An Intelligent Approach for Context-Aware Service Selection using Machine Learning

Service selection is a process to choose the services that best suit user functional and Non-functional Properties (NFP). With the increasing number of available services, users are offered a choice of competitively functional (or even identical) services. Therefore, this choice strongly depends on the NFPs and the user preferences (context) that differentiate between several competitive services. The service selection can be performed automatically and transparently to the user. In this paper, An extension of OWL-S service is proposed to take context information into account during the selection. Afterwards, we presents an intelligent approach for context-aware service selection based on Markov Decision Process, we show how to solve it using reinforcement learning techniques.

Support Cloud SLA Establishment Using MDE

In the last decade, Service Level Agreements (SLAs) play a pivotal role in Cloud Computing especially for guaranteeing quality, availability and responsibility. SLA involves different actors including customers and service providers. The problem that arises is how to establish an SLA contract between those actors and especially how to help the customer to choose the provider that offers the adequate services. Another important point is the measures to guarantee that the provider respects its contract with the consumer. Our approach embraces model driven engineering principles to automate the generation of the SLA contract and its real-time monitoring. For this purpose, we propose three languages dedicated respectively to the customer, the supplier, and the contract specification. Since we cannot predict QoS values at advance, we propose to use machine learning to learn QoS behavior at run-time.

A process for maintaining heterogeneous models consistency through change synchronization

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.