A context-aware intentional service prediction mechanism in PIS

International audiencePervasive Information System (PIS) represents a new generation of Information Systems (IS) available anytime, anywhere in a pervasive environment. In this paper, we propose to enhance PIS transparency and efficiency through a context-aware intentional service prediction approach. This approach allows anticipating user's future needs, offering and recommending him the most suitable service in a transparent and discrete way. We detail in this paper our service prediction mechanism and present encouraging experimental results demonstrating our proposition

Analyse des mécanismes de découverte de services avec prise en charge du contexte et de l'intention

National audienceAvec la démocratisation des dispositifs et des réseaux mobiles, les systèmes d'information deviennent pervasifs. Les utilisateurs de ces systèmes doivent désormais évoluer dans de véritables espaces de services, dans lesquels plusieurs services sont offerts. Afin d'améliorer la transparence des systèmes d'information pervasifs, nous proposons une nouvelle approche pour ces systèmes, à la fois sensible au contexte et intentionnelle. Dans cette approche, les services offerts par ces systèmes sont proposés afin de satisfaire à une intention, laquelle correspond à l'expression d'un but utilisateur, dans un contexte donné. Pour valider cette approche, nous développons actuellement une plate-forme de découverte de services basée sur une extension de OWL-S qui intègre ces notions. Grâce à des résultats expérimentaux, nous analysons l'impact de l'usage de ces notions de contexte et d'intention dans la sélection de services et démontrons l'intérêt de notre approche dans la découverte des services

Clustering Service Networks with Entity, Attribute, and Link Heterogeneity

Many popular web service networks are content-rich in terms of heterogeneous types of entities and links, associated with incomplete attributes. Clustering such heterogeneous service networks demands new clustering techniques that can handle two heterogeneity challenges: (1) multiple types of entities co-exist in the same service network with multiple attributes, and (2) links between entities have diverse types and carry different semantics. Existing heterogeneous graph clustering techniques tend to pick initial centroids uniformly at random, specify the number k of clusters in advance, and fix k during the clustering process. In this paper, we propose Service Cluster, a novel heterogeneous service network clustering algorithm with four unique features. First, we incorporate various types of entity, attribute and link information into a unified distance measure. Second, we design a Discrete Steepest Descent method to naturally produce initial k and initial centroids simultaneously. Third, we propose a dynamic learning method to automatically adjust the link weights towards clustering convergence. Fourth, we develop an effective optimization strategy to identify new suitable k and k well-chosen centroids at each clustering iteration. Extensive evaluation on real datasets demonstrates that Service Cluster outperforms existing representative methods in terms of both effectiveness and efficiency

Intention Prediction Mechanism In An Intentional Pervasive Information System

International audienceNowadays, the development of pervasive technologies has allowed the improvement of services availability. These services, offered by information systems (IS), are becoming more pervasive, i.e., accessed anytime, anywhere. However, those pervasive information systems (PIS) remain too complex for the user, who just wants a service satisfying his needs. This complexity requires considerable efforts from the user in order to select the most appropriate service. Thus, an important challenge in PIS is to reduce user's understanding effort. In this chapter, we propose to enhance PIS transparency and productivity through a user-centred vision based on an intentional approach. We propose an intention prediction approach. This approach allows anticipating user's future requirements, offering the most suitable service in a transparent and discrete way. This intention prediction approach is guided by the user's context. It is based on the analysis of the user's previous situations in order to learn user's behaviour in a dynamic environment

Enriched Semantic Service Description for Service Discovery: Bringing Context to Intentional Services

International audienceIn service-orientation, the notion of service is studied from different point of views. On the one hand, several approaches have been proposing services that are able to adapt themselves according to the context in which they are used. On the other hand, some researches have been proposing to consider user intentions when proposing business services. We believe that these two views are complementary. An intention is only meaningful when considering the context in which it emerges. Conversely, context description is only meaningful when associated with a user intention. In order to take profit of both views, we propose to extend the Ontology Web Language for services description (OWL-S). We include on it both the specification of context associated with the service and the intention that characterize it. This extended description is experimented in a semantic registry that we built for service discovery purposes. Such registry considers a matching algorithm, which exploits the extended description. Then, we present experimental results of this matching algorithm that demonstrates the advantages one may have on using the proposed descriptor. Thus, we propose a new vision of service orientation taking into account the notion of intention and context. This new vision is based on the extended semantic descriptor, which is necessary in order to enhance transparency of the system by proposing to the user the most appropriate service

Service discovery and prediction on Pervasive Information System

International audienceRecent evolution of technology and its usages, such as BYOD (Bring Your Own Device) and IoT (Internet of Things), transformed the way we interact with Information Systems (IS), leading to a new generation of IS, called the Pervasive Information Systems (PIS). These systems have to face heterogeneous pervasive environments and hide the complexity of such environment end-user. In order to reach transparency and proactivity necessary for successful PIS, new discovery and prediction mechanisms are necessary. In this paper, we present a new user-centric approach for PIS and propose new service discovery and prediction based on both user's context and intentions. Intentions allow focusing on goals user wants to satisfy when requesting a service. Those intentions rise in a given context, which influence the service implementation. We propose a service discovery mechanism that observes user's context and intention in order to offer him/her the most appropriate service satisfying her/his intention on the current context. We also propose a prediction mechanism that tries to anticipate user's intentions considering the user's history and the observed context. We evaluate both mechanisms and discuss advanced features future PIS will have to deal with

Cross-formalism resource discovery in smart environments

Nowadays, the Internet of Things (IoT) is becoming progressively colloquial to media. However, when there are trillions of resources out there, how can we spontaneously specify the resource we need? Therefore, one of the main research questions is the device and service discovery. Many standard web services descriptions are used to describe not only web services but also physical devices. These devices are encapsulated under the web service communication layer to make them available on the Internet. This technique enables automatic discovery, configuration, and execution of resources in dynamic environments. Thus, we focus on the resource description language that allows semantic annotation. Nevertheless, there is no single standard formalism to describe resources. It is more tactful to handle multiple description formalisms simultaneously. This thesis presents a cross-formalism resource discovery technique which utilizes the user context and resources context to improve the recommendation of resources. The discovery process should not be restricted to single resource description formalism. Moreover, the matching algorithm should be user-aware and environmentally adaptive, i.e. depending on the users current situation, rather than limit to keyword-based search. This thesis explains the implementation detail and shows the evaluation of each implemented module. We aimed to prove that the quality of the result is improved significantly compared to conventional discovery techniques. To demonstrate the usability of the proposed method, we deploy it in MERCURY. MERCURY is a platform that allows both businesses to engage with their customers and end users to create custom-made applications. Within the context of MERCURY, registration, assembling, and execution of resources need the automatic resource discovery. Since the implementation of this work is designed to be a standalone service, there is no restriction to use it under the domain of MERCURY