Virtual Operating room characterizes a large artificial environment of interaction in the medical context. It enables a better simulation for different surgical scenarios through 3D (three‐dimensional) objects. Virtual Reality (VR) uses computer technology to develop these virtual simulated applications. It brings valuable innovations in different fields. It is integrated also to enhance healthcare and therapies. To improve and assist on medical VR applications, ontologies would be useful. They can unify the content of these applications and facilitate their implementation via semantic rules. Also, ontologies can be applied to realize an effective VR knowledge modeling. This paper presents a virtual simulation of an operating room taking advantage of the semantic representation. The goal of this study is to propose a novel ontological VR system that models an operating room and its components. The execution of the proposed method was proved by the developed ontology OROnto (Operating Room Ontology). This ontology was useful for modeling hospital scenarios and the construction of a valuable VR system. To demonstrate the proposed approach feasibility and performance, we have implemented the Operating Room Virtual Integration Process Using Ontology (ORVIPO) prototype. Compared to different other ontological methods and related works, our approach have shown interesting findings such as recall (71%), precision (83%), and F-measure (76%).

The massive use of ontologies generates a large amount of semantic data. To facilitate their management, persistent solutions for storing and querying these semantic data loads have been proposed. This gave rise to a new type of databases, called ontology-based databases (OBDB). In recent years, the need for data and real-time services has increased significantly in a large number of applications. However, the OBDB does not implement any mechanism to address real-time applications which are characterized, not only by handling large amounts of data, but also by temporal constraints, to which can be submitted data and treatments. As well, geographically extended applications, requiring using real-time databases that manage data and distributed processing are increasingly needed.These applications are managed by Distributed Real-Time DataBase Management System (DRTDBMS). Like any system, the DRTDBMS, often go through overload phases, due to the unpredictable arrival of transactions submitted by users. In order to better manage Quality of Service (QoS) in these systems by facing instability periods, approaches based on Distributed Feedback Control Scheduling (DFCS) were proposed. These approaches does not address the use of ontological data. In this paper, we propose an approach aiming to enhance QoS in DRTDBMS based on data replication. It consists in extending the DFCS architecture by the manipulation of ontological data as well as handling the execution of accessing transactions. In the extension we propose, we study the applicability of different data replication policies. The proposed architecture is then called Replication-Based-Distributed Feedback Control Scheduling Architecture for Real-Time Ontology (Replication-Based-DFCS-RTO). We also show the contribution provided by our approach through simulation results.

Abstract The emergence of the Internet of Things (IoT) in the medical field has led to the massive deployment of a myriad of medical connected objects (MCOs). These MCOs are being developed and implemented for remote healthcare monitoring purposes including elderly patients with chronic diseases, pregnant women, and patients with disabilities. Accordingly, different associated challenges are emerging and include the heterogeneity of the gathered health data from these MCOs with ever‐changing contexts. These contexts are relative to the continuous change of constraints and requirements of the MCOs deployment (time, location, state). Other contexts are related to the patient (medical record, state, age, sex, etc.) that should be taken into account to ensure a more precise and appropriate treatment of the patient. These challenges are difficult to address due to the absence of a reference model for describing the health data and their sources and linking these data with their contexts. This article addresses this problem and introduces a semantic‐based context‐aware system (IoT Medicare system) for patient monitoring with MCOs. This system is based on a core domain ontology (HealthIoT‐O), that is, designed to describe the semantic of heterogeneous MCOs and their data. Moreover, an efficient interpretation and management of this knowledge in diverse contexts are ensured through SWRL rules such as the verification of the proper functioning of the MCOs and the analysis of the health data for diagnosis and treatment purposes. A case study of gestational diabetes disease management is proposed to evaluate the effectiveness of the implemented IoT Medicare system. An evaluation phase is provided and focuses on the quality of the elaborated semantic model and the performance of the system.

In this paper, we propose a Multidimensional Business Ontology based-Premodeling System (MBOPS). Its knowledge-oriented core ensures an easier use and share for unambiguous business process modeling. Furthermore, the proposed (MBOPS) guarantees a good understanding level for the modelled-domain familiar designers as well as the modelled-domain non-familiar designers thanks to a Multidimensional business ontology. In order to evaluate the proposed (MBPS) and demonstrate its effectiveness, we choice the breast cancer treatment protocols as an applicative field.

Over the last decade, ontologies have been increasingly used in recommender systems. Domain ontologies are mainly used to analyze the user behaviour with reference to knowledge structure in order to build user profiles. In addition, ontologies are useful for modelling the trust between users extracted from a social network. We, accordingly, propose to combine two approaches to improve the recommendation process in tourism domain. The first is the trusted friends' preference based on the assumption that users generally have a tendency to use items recommended by friends rather than strangers. The second is the ontology based user interest to represent the semantics of these interests. A Trusted Friends' Application is developed to determine social trusted friends by analyzing user's profile on Facebook. Afterwards, we have represented the user's model as an ontology that takes into consideration all trusted friends' preferences and the degree of trust between friends. Finally, we have used this ontology in a knowledge-based tourism recommender system as a smart e-tourism tool able to recommend items based on the users' preferences and their trusted friends' preferences.

Internet of Things (IoT) covers a variety of applications including the Healthcare field. Consequently, medical objects become connected to each other with the purpose to share and exchange health data. These medical connected objects raise issues on how to ensure the analysis, interpretation and semantic interoperability of the extensive obtained health data with the purpose to make an appropriate decision. This paper proposes a HealthIoT ontology for representing the semantic interoperability of the medical connected objects and their data; while an algorithm alleviates the analysis of the detected vital signs and the decision-making of the doctor. The execution of this algorithm needs the definition of several SWRL rules (Semantic Web Rule Language).

The enormous exploitation of connected objects for patient monitoring becomes a source of a huge quantity of health data. This data can be badly expressed, understood, and exploited by other systems and devices. Additionally, the huge amount of connected objects embeds with resource-constrained devices (sensors, actuators, RFIDS, etc.) are also challenging. Consequently, semantic representation of both medical connected objects and their data becomes important. Furthermore, the semantic reasoning of this data requires the definition of diverse rules in order to provide adequate and efficient results. This paper suggests two main contributions. The first one focuses on the semantic representation of both the medical connected objects and their data by proposing a HealthIoT ontology. The second contribution is proposed to provide practical backup to the use of this ontology. In fact, an IoT Medicare system is proposed to evaluate the HealthIoT ontology.

When creating social recommender systems, trust between various users in social networks emerges as an essential decisive feature. This paper aims at calculating trust among users by identifying all possible relations that may exist among those users and evaluate them. Although many models were proposed to analyze computational trust in different applications of social web, little importance is given to the time factor even by models that represent trust as a source of recommendation measurement. Emphasized in this paper is the temporal factor and its role in improving the accuracy of trust in social recommender systems. We propose to integrate the temporal factor in measuring trust between social network friends. A Trusted Friends' Facebook application is developed to demonstrate the importance of time in the users' interactions for determining social trusted friends. After that, this application will be used in a semantic social tourism recommender system as a smart e-tourism tool to motivate users to travel to Tunisia for medical purposes.

Medical tourism is a domain rich of data that are stored in many hundreds of data sources. Many of these sources should be used during the development of tourism information systems. That is why, it is very important to create a referential model that represents the medical tourism in Tunisia. The e-tourism ontology provides a way to achieve integration and interoperability through the use of shared vocabulary and meanings for terms with respect to other terms. In this paper, we will create an ontology as a very expressive hierarchy of categories, populating with information from Tunisian medical tourism providers' services that represent all concepts and relations of the medical tourism in Tunisia. This ontology will be integrated in a trust based recommender system to deal with the lack of semantic information in personalized recommendation in tourism domain.

This work deals with the Business Process Management (BPM) and the Knowledge Management (KM). Indeed, the knowledge has a fairly important role in the business process modeling. This importance leads us to talk about the business knowledge notion which can be decomposed in many dimensions. In this paper, we first define business knowledge and its different dimensions. Then, we propose a multidimensional business knowledge model using a set of rules that formalize of these dimensions organized around the three-key perspectives of business processes: (i) process perspective, (ii) organizational perspective and (iii) informational perspective. In this way, we have used ATL (Atlas Transformation Language) language, as a means to implement the whole of formalization rules, so as to construct the target model starting from the business process model(s).