Bio: Salma Sassi is an academic researcher from University of Jendouba. The author has contributed to research in topics: Computer science & Ontology (information science). The author has an hindex of 4, co-authored 28 publications receiving 62 citations.
••01 Oct 2017
TL;DR: A new platform for health care, designed primarily for the semantization of the Internet of things in the medical and healthcare field, which provides various services, like: Multi-type functional communication service, the significant exploitation of the localization feature provided by the connected objects, the simplification of medical texts for the patients and the effective integration of the social media technology.
Abstract: A gradual evolution of the Internet, allowing it to extend beyond the electronic world to the physical world by interconnecting various devices and sensors that can communicate with each other and share data. The field of healthcare monitoring Systems have experienced significant changes using this promising new technology, called Internet of Things (IoT). In this paper, we propose a new platform for health care, which we have called the ’Semantic Medical IoT platform’. This platform is designed primarily for the semantization of the Internet of things in the medical and healthcare field. It proposes solutions for the problems of the interoperability of medical devices, the integration of massive and heterogeneous data, and the personalized visualization of these data. The platform also provides various services, like: Multi-type functional communication service, the significant exploitation of the localization feature provided by the connected objects, the simplification of medical texts for the patients and the effective integration of the social media technology. The SM-IoT Platform also defines new contract-based security policies to ensure the confidentiality of patient’s health information.
••01 Nov 2017
TL;DR: The SM-IoT platform is able to collect data from heterogeneous information sources, integrate them by using a flexible semantic web, store them in the cloud for further analysis, visualized these data with user-friendly interfaces and facilitate their sharing by taking into account their privacy aspect.
Abstract: Today, numerous technological advances in electronic and information technologies are rapidly transforming our modern life. Industries are being completely transformed and health systems are not oblivious to these changes. All these new technologies are opening up a wide range of new opportunities and challenges for researchers, physicians and patients. The emergent paradigm of Internet of Things is one of the promising technologies introduced into the world of health care. In fact, IoT has been widely applied to interconnect available medical devices and sensors which allow patients to take control of their health condition in real time, also physicians to accurately remotely monitor the health of their patients. The ultimate goal of achieving high quality of healthcare practices depends on the ability to effectively integrate data incoming from heterogeneous sources, share the collected data while keeping their security and privacy, use powerful data analytics tools to extract useful information from these data, and the ability to have an expressive and personalized visualization. In this paper, we propose the SM-IoT platform, an IoT-based platform for intelligent and personalized healthcare, dedicated to patients, as well as caregivers. The aim of this platform is to improve the remote patient monitoring and promote healthcare services. SM-IoT platform is able to collect data from heterogeneous information sources, integrate them by using a flexible semantic web, store them in the cloud for further analysis, visualized these data with user-friendly interfaces and facilitate their sharing by taking into account their privacy aspect.
••11 Jul 2016
TL;DR: This paper proposes a graph partitioning method for large dynamic graphs, an implementation of the proposed approach on top of the AKKA framework is presented, and it is experimentally shown that the approach is efficient in the case of largeynamic graphs.
Abstract: Distributed graph processing has become a very popular research topic recently, particularly in domains such as the analysis of social networks, web graphs and spatial networks. In this context, graph partitioning is an important task. Several partitioning algorithms have been proposed, such as DFEP, JABEJA and POWERGRAPH, but they are limited to static graphs only. In fact, they do not consider dynamic graphs in which vertices and edges are added and/or removed. In this paper, we propose a graph partitioning method for large dynamic graphs. We present an implementation of the proposed approach on top of the AKKA framework, and we experimentally show that our approach is efficient in the case of large dynamic graphs.
••01 Jan 2020
TL;DR: A flexible semantic integration framework for IoT, unstructured and structured healthcare data in EHR systems called SF4FI-EHR is proposed, built on a novel approach that applying semantic web technologies and the HL7 FHIR standard to handle integration challenges.
Abstract: Despite the huge efforts focused on EHR development and massive many years of widespread availability of this latter, health care providers and organizations are still looking for innovative solutions to bring in all IoT data, and unstructured data into electronic health record (EHR) systems. There is a growing need to semantically integrate health-related data from different sources to support decision-making and improve the quality of care services provided. In this paper, we propose a flexible semantic integration framework for IoT, unstructured and structured healthcare data in EHR systems called SF4FI-EHR. It is built on a novel approach that applying semantic web technologies and the HL7 FHIR standard to handle integration challenges. Our experiment results from the proof-of-concept study show that the use of such approach does enhance healthcare data integration as well as overcome obstacles that prevent the optimal exploitation of these data.
01 Nov 2018
TL;DR: This paper proposes a probabilistic approach to model and interpret uncertain web resources, and presents operators to compute the uncertainty for the response and proposes algorithms in order to validate resources and to achieve the uncertain navigation.
Abstract: Navigating through the web of uncertain data has become increasingly difficult. Unfortunately, the old techniques used in the classical web can not handle the navigation of uncertain web data or resources. Uncertain data published on the web can be heterogeneous, conflicting, inconsistent or in incompatible formats. This uncertainty is inherently related to many factors such as information extraction and data integration. In order to give the web user the best experience and provide him with the most relevant answer we have to consider the uncertainty of web data and model it. In this paper, we propose a probabilistic approach to model and interpret uncertain web resources. We present operators to compute the uncertainty for the response. Finally, we propose algorithms in order to validate resources and to achieve the uncertain navigation.
TL;DR: How innovative IoT enabled technologies like cloud computing, fog computing, blockchain, and big data can be used to leverage modern healthcare facilities and mitigate the burden on healthcare resources is discussed.
Abstract: The Internet of Things (IoT) is becoming an emerging trend and has significant potential to replace other technologies, where researchers consider it as the future of the internet. It has given tremendous support and become the building blocks in the development of important cyber-physical systems and it is being severed in a variety of application domains, including healthcare. A methodological evolution of the Internet of Things, enabled it to extend to the physical world beyond the electronic world by connecting miscellaneous devices through the internet, thus making everything is connected. In recent years it has gained higher attention for its potential to alleviate the strain on the healthcare sector caused by the rising and aging population along with the increase in chronic diseases and global pandemics. This paper surveys about various usages of IoT healthcare technologies and reviews the state of the art services and applications, recent trends in IoT based healthcare solutions, and various challenges posed including security and privacy issues, which researchers, service providers and end users need to pay higher attention. Further, this paper discusses how innovative IoT enabled technologies like cloud computing, fog computing, blockchain, and big data can be used to leverage modern healthcare facilities and mitigate the burden on healthcare resources.
TL;DR: It has been demonstrated that larger and costlier healthcare centre projects usually have higher quality designs with fewer critical errors, independently of their geographical location and construction year.
TL;DR: This paper presents bladyg, a graph processing framework that addresses the issue of dynamism in large-scale graphs and experimentally evaluates the performance of the proposed framework by applying it to problems such as distributed k-core decomposition and partitioning of large dynamic graphs.
TL;DR: Bladyg as discussed by the authors is a block-centric framework that addresses the issue of scale and dynamism in large-scale graphs, and it is implemented on top of the Akka framework.
Abstract: Recently, distributed processing of large dynamic graphs has become very popular, especially in certain domains such as social network analysis, Web graph analysis and spatial network analysis. In this context, many distributed/parallel graph processing systems have been proposed, such as Pregel, GraphLab, and Trinity. These systems can be divided into two categories: (1) vertex-centric and (2) block-centric approaches. In vertex-centric approaches, each vertex corresponds to a process, and message are exchanged among vertices. In block-centric approaches, the unit of computation is a block, a connected subgraph of the graph, and message exchanges occur among blocks. In this paper, we are considering the issues of scale and dynamism in the case of block-centric approaches. We present bladyg, a block-centric framework that addresses the issue of dynamism in large-scale graphs. We present an implementation of BLADYG on top of akka framework. We experimentally evaluate the performance of the proposed framework.
TL;DR: In this article, a literature review on the application of the Internet of Things (IoT) in the health domain is presented. But, despite the extensive literature about this topic, the application in healthcare scarcely covers requirements of this sector.