Mahmoud Nassar

Context modeling and reasoning for building context aware services

The rapid evolution of software paradigms, wireless technologies and mobile devices induced the emergence of new applications called context-aware services oriented applications. Such applications provide appropriate services or relevant information to the user or other applications to perform a specific task. An important challenge in such applications development process is context modeling and reasoning. Ontology based models presents the most promising instrument for context modeling and offers possibility of applying ontology reasoning techniques. the aim of context reasoning is converting low-level context to highlevel context in order to provide customized services to users. In this paper, we present an architecture for the development of context aware services based on ontologies. We also highlight our context metamodel and discuss about reasoning process for e-health systems.

Modeling Ad-Hoc Collaboration for Automated Process Support

Understanding and supporting collaboration is a major concern in any collective endeavor. In software engineering, planning collaboration is difficult and often occurs rather ad-hoc. However, many proposals for the description and support of collaboration assume necessary information is readily available and already integrated in process models. We believe practitioners should be given the ability to represent their evolving understanding about collaboration and should be supported by tools using such representation. We developed an extension to the Software & System Process Engineering Meta-Model (SPEM), which introduces concepts needed to represent precise and dynamic ad-hoc collaboration setups. We also describe tool support and present a proof-of-concept based on a fictitious but realistic application example.

A Generic Traceability Framework for Model Composition Operation

In order to handle complexity, model driven engineering aims at building systems by developing several models, where each model represents a specific concern of the system. In this context, designers need mechanisms to validate, synchronize and understand interactions between those perspectives. Model composition deals with these issues but remains a complex task. For these reasons, we believe that a strong traceability mechanism is a key factor to handle relationships between models and manage the complexity of the composition operation. This paper describes a generic approach to keep track of the model composition operation. We also define a traces generation process to adapt our proposal to any specific composition language. Finally, an example is presented to illustrate our contributions.

A Governance Based Architecture for Enterprise Cloud Computing Adoption

In the trendy wide adoption of the cloud computing solutions, the relevant challenge has changed from the opportunities and capabilities offered to the governance issues when moving to this new computing paradigm. Since, the enterprises are considerably interested about keeping control even in the virtual environment. To fulfill this objective an architectural approach is introduced: Governance-based Architecture for Enterprise Cloud Computing Adoption. As an alternative to the big organisms Cloud Computing Reference Architecture (CCRA) which is provider-centric, the proposed architecture is consumer-centric and tries to deal with the enterprise requirements and needs. The core architecture is articulated in seven modules which are enveloped by a Unified Cloud Service Interface.

A traceability approach for model composition

Model composition is one of the important activities in model driven engineering, because complex systems are built based on several viewpoints. The composition operation is a tedious task, however, traceability not only provides us with the tool to comprehend its effects, but also helps us to better manage the operation itself. This paper suggests a traceability framework for the model composition operation based on aspect oriented modeling and graph transformation. The generated trace models conform to a generic metamodel that allows representing nested trace links. A merge specification case study is presented to illustrate our contribution.

MiCATS: Middleware for Context-Aware Transactional Services

In context-aware service-based environments, fulfilling user’s recommendations bring about several challenges that are mainly due to the dynamic nature and limited underlying resources of these environments. In this paper, we mainly focus on the transactional aspects of context-aware services. To cope with the aforementioned issues, we opt for a middleware-based solution (MiCATS). We therefore present our novel approach for managing context-aware transactions which allows for adaptation to the requirements of applications and mobile context in terms of transactional properties. Then, we introduce our context-aware transactional service model (CATSM) and a context-aware architecture based on adaptation policies. The proposed contributions are implemented within a middleware platform.

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.

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.

Using Context Ontology and Linear SVM for Chronic Kidney Disease Prediction

In the e-Health learning area, the use of chronic patient context has become very important given the increase in the number of individuals who suffer from these diseases and the unavailability of medications. Specifically, chronic kidney failure is one of the diseases that goes undetected and undiagnosed until it is well advanced. The need for preventive prediction remains an essential task for the well-being of patients at risk. In this paper, we aim to explore the added value of using ontology-based prediction, focusing on Linear SVM, to deal with Chronic Kidney Disease Problems.