Hatim Guermah

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.

Towards an ontology based architecture for context-aware services composition

An important challenge in pervasive computing environments is to automatically enable software applications by dynamically composing services. Service composition is a way to plan a business process to fulfill business goals that cannot be achieved by individual business services. Given a representation of services in OWL-S, service composition can be modeled as an Artificial Intelligence planning (AI planning) problem. Based on the initial context and the goal context, planning-based service composition launches a goal-oriented composition procedure to generate a plan for the service composition. In this paper, we present an overview of the architecture for the composition of context aware services based on ontologies, we also present our Composition Planner that allows to generate a plan corresponding to the request of the users according to their context.

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.

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.

How Can Semantics and Context Awareness Enhance the Composition of Context-aware Services?

The context-aware services refers to applications that use so-called contextual information to provide appropriate services or relevant information to the user or other applications to perform a specific task. An important challenge in context-aware service oriented systems is the creation of a new service on demand to carry out more complex tasks through the composition of existing services. In this work, we aim to propose a semantic based architecture for the development of context aware services composition using Artificial Intelligence (AI) planning. The straightforward translation between AI planning through PDDL and Semantic web services via OWL-S allows to automate the composition process. Thus planning based service composition launches a goal-oriented composition procedure to generate a plan of composite service corresponding to the user request.

Towards a semantic approach for discovering context aware services

The current service discovery mechanisms are syntactic methods that often offer poor quality results. By researching this area we are able to identify issues that limit the quality of the result with these traditional service mechanisms. To overcome this issue we use ontologies to enable semantic matchmaking. By reasoning on concepts and relations, our approach can derive matches that syntactically would not match. Additionally, by introducing contextual information in the service discovery process, the quality of the service discovery result may improve. In this paper, we present an overview of the architecture for the discovery of context aware services by using ontologies, we also present our context aware semantic service by extending OWL-S with context properties.

A Semantic Approach for Service Adaptation in Context-aware Environment☆

Abstract With pervasive computing, Service oriented architectures become more mobile and distributed and are executed in varying contexts. Context is understood as any information about the user needs and operating environment which vary dynamically and have an impact on design choices. Context aware Services oriented systems have emerged as an underlying design and development paradigm. An important challenge in such systems is context modeling, reasoning and adaptating services behavior. Using ontologies and semantic web services to deal with context aware services has attracted a lot of interest. In this paper, we present an architecture for the development of context aware services based on ontologies. We highlight our context metamodel and discuss about reasoning process. we also present our semantic approach for service adaptation in context aware environment.

Ontology Based Context Modeler for Context-Aware Systems (Short paper)

In ubiquitous computing, context-aware service oriented systems refer to applications that use so-called contextual information to provide appropriate services or relevant information to the user or other applications to perform a specific task. An important challenge in such applications is context modeling, using ontologies to model context information and to reason about context at a semantic level has attracted a lot of interest in the research community. However, most of the presented approaches do not usually take into account any software engineering method. Therefore, employing standard approaches to formulate the development process becomes of importance. In this paper, we present an overview of the architecture for the development of context aware services based on ontologies, we also present our context modeler based on ODM (Ontology Definition Metamodel) and model transformations that allows to generate an OWL ontology representing context information.