Yacine Sam

Diamond - A Cube Model Proposal based on a Centric Architecture Approach to Enhance Liquid Software Model Approaches.

The adoption of multiple connected devices in our lives is a reality which the available technology is not able to deal with. The concept of Liquid Software emerged in the end of the 90s, however, its full potential of a unified interface which can drift between different connected devices and bring with its behavior and complexities is still not fully applied. Thus, enhancements of current Web application architecture, in other words, a new approach able to deal with our technology requirements is required. In this context, we propose a centric-basic architecture to deal with Liquid Software principles and constraints. The CUBE, once built, should be able to deal with all these requirements, making use of best practices from different technologies.

LODS: A Linked Open Data Based Similarity Measure

With the rapid evolution of Linked Open Data (LOD), researchers are exploiting it to solve particular problems such as semantic similarity assessment. Existing LOD-based semantic similarity approaches attach compared data (terms or concepts) to LOD resources to exploit their semantic descriptions and relationships with other resources and estimate the degree of overlap between resources. Current approaches suffer from two limitations: they focus on the analysis of links between resources and ignore the important taxonomic structure of concepts and categories used to describe resources. On the other hand, they do not exploit interlinks between LOD resources in order to enrich data used to compute the similarity score. In this paper, we overcome the above limitations by proposing a new LOD-based similarity measure based on the combination of ontological, classification and property dimensions of LOD resources.

SmartLoire: A Web Mashup Based Tool for Personalized Touristic Plans Construction

A large set of Web services are nowadays available providing complementary content about: schedule of public transportation, description of touristic area, eteorological information, geocoding operations, etc. We present in this article a mobile-application prototype that mashups Web services to define custom touristic plans, i.e., takes into account tourist preferences (type of stay, interest) and constraints (duration, budget, locomotion modality). Moreover, the application considers real-time events (e.g., weather data or temporary unavailability of a touristic site) to adapt proposed schedules.

Context-Aware and Linked Open Data Based Service Discovery

Context-aware Service Discovery process is the task of identifying relevant services that could satisfy user needs while considering contextual information. Depending on such context information, the discovery process may become a challenging task as it requires to consider different kinds of constraints. In tourism domain, for example, context-aware service discovery process has to take into account functional information (what functionalities does a service offer) and non-functional information (such as user profile and preferences or information about her/his surrounding environment). To deal with context-aware services discovery issue, researchers proposed solutions that rely on semantic Web technologies and particularly ontologies to describe and discover services. However, ontologies are generally not-interconnected and limited because they focus on specific domains. In this paper, we propose a novel discovery approach for context-aware services which relies on Linked Open Data (LOD) instead of ontologies. First, we propose a model to describe both functional and non-functional properties of context-aware services. Then, we enrich the service model instances with semantic data from LOD. Based on enriched descriptions, we propose matchmaking algorithms to satisfy a user’s needs in a given context. We performed experiments on touristic services. Results show that using LOD for context-aware service discovery is promising regarding discovery effectiveness.

A smart MobiWeb mashup trip planner tool

A wide range of Mobile Web-based applications are nowadays being built and proposed as mashup applications. Their aim is to be intuitive enough to be easily created by end-users directly selecting interactive elements, content and functionalities from existing resources, without requiring any technical skills. We propose in this article a Mobile Web Trip Planner Tool that mashups heterogeneous online travel information provided from multiple Restful Web services to recommend custom visit plans. This application takes into account tourist preferences (type of stay, interest) and constraints (duration, budget, locomotion modality). Moreover, it considers real-time events (e.g., weather data or temporary unavailability of a touristic site) to adapt proposed schedules.

Visual Configuration for RESTful Mobile Web Mashups

The fast development of powerful mobile devices and rich Internet applications have boosted the production of Mobile Web applications designed to support end-users in their daily activities using smartphones. When these applications are the result of combining multiple heterogeneous data and services, the traditional one-size-fits-all development approach is not convenient since it does not consider the specificities of each potential user. New techniques and tools are then required to offer applications that better fit end-users constraints, preferences, and contexts while allowing them creating, consuming and sharing added-value services. We present in this article a novel mashup approach based on configuration theory and a visual tool that achieves this goal. As a proof of concept, we present an implementation scenario in the tourism domain.

CUBE System: A REST and RESTful Based Platform for Liquid Software Approaches

In the last years, a multi-host environment, that means, having at our disposal multiple connected devices, had become common in our daily lives. We are inundated with many services and applications with a promise to enhance our daily. However, the opposite occurs. We spend too much time and effort to use the same services in all multi-hosts we own. For dealing with this gap, we propose a model based on some principles of Service-Oriented Architecture (SOA) to achieve Liquid Software concepts. This approach requires a service design with an innovate and a unified interface which can drift between different connected devices and bring us with its behavior and complexities. In this context, based on REST and RESTful principles and constraints we propose an innovate centric-basic architecture to deal with multi-hosts. The CUBE, once finished, will allow that both services and multi-hosts discovery for migration across its system to enhance the usability of all devices owned by the same user.

IoT Mashups: From IoT Big Data to IoT Big Service

Internet of Things (IoT) addresses the challenge to provide a transparent access to a huge number of IoT resources that can be either physical devices or just data resources. Moreover, because of the large number of resource-constrained devices and the dynamic nature of IoT environments, integrating the resulted data becomes a non trivial task. We believe that the use of mashups, a way to compose new services from existing ones, can be a solution to the above challenge if each resource exposes its functionalities as a Web service. In the IoT environment, this will constitute a Web of Things where mashups development will take advantage of the connected physical world. The huge amounts of IoT-generated data from physical devices and data sources, called IoT Big Data, requires new design solutions to speed up data processing, scale up with the data volume and improve data adaptability. Besides existing techniques for IoT data collection, filtering, and analytics, we present in this article a mashup oriented model, called IoT Big Services, for provisioning data-centric IoT services in the context of IOT mashups. These IoT services are organized in tree structure where each node, called an IoT Big Service, acts as an integrator that collects data from lower level, processes them and delivers the results to higher level in the architecture.