1,126 research outputs found

    A Semantic Web approach to ontology-based system: integrating, sharing and analysing IoT health and fitness data

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    With the rapid development of fitness industry, Internet of Things (IoT) technology is becoming one of the most popular trends for the health and fitness areas. IoT technologies have revolutionised the fitness and the sport industry by giving users the ability to monitor their health status and keep track of their training sessions. More and more sophisticated wearable devices, fitness trackers, smart watches and health mobile applications will appear in the near future. These systems do collect data non-stop from sensors and upload them to the Cloud. However, from a data-centric perspective the landscape of IoT fitness devices and wellness appliances is characterised by a plethora of representation and serialisation formats. The high heterogeneity of IoT data representations and the lack of common accepted standards, keep data isolated within each single system, preventing users and health professionals from having an integrated view of the various information collected. Moreover, in order to fully exploit the potential of the large amounts of data, it is also necessary to enable advanced analytics over it, thus achieving actionable knowledge. Therefore, due the above situation, the aim of this thesis project is to design and implement an ontology based system to (1) allow data interoperability among heterogeneous IoT fitness and wellness devices, (2) facilitate the integration and the sharing of information and (3) enable advanced analytics over the collected data (Cognitive Computing). The novelty of the proposed solution lies in exploiting Semantic Web technologies to formally describe the meaning of the data collected by the IoT devices and define a common communication strategy for information representation and exchange

    Integration of heterogeneous data sources and automated reasoning in healthcare and domotic IoT systems

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    In recent years, IoT technology has radically transformed many crucial industrial and service sectors such as healthcare. The multi-facets heterogeneity of the devices and the collected information provides important opportunities to develop innovative systems and services. However, the ubiquitous presence of data silos and the poor semantic interoperability in the IoT landscape constitute a significant obstacle in the pursuit of this goal. Moreover, achieving actionable knowledge from the collected data requires IoT information sources to be analysed using appropriate artificial intelligence techniques such as automated reasoning. In this thesis work, Semantic Web technologies have been investigated as an approach to address both the data integration and reasoning aspect in modern IoT systems. In particular, the contributions presented in this thesis are the following: (1) the IoT Fitness Ontology, an OWL ontology that has been developed in order to overcome the issue of data silos and enable semantic interoperability in the IoT fitness domain; (2) a Linked Open Data web portal for collecting and sharing IoT health datasets with the research community; (3) a novel methodology for embedding knowledge in rule-defined IoT smart home scenarios; and (4) a knowledge-based IoT home automation system that supports a seamless integration of heterogeneous devices and data sources

    Open data observatories: a survey

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    Open Data Observatories refer to online data platforms that provide free, real-time and historical data. They facilitate collaborative and unified environments for citizens and applications, supplemented with reusable datasets, analysis tools and interactive visualisations. Open Data Observatories collect and integrate various data types from multiple disparate providers. Data types include variables such as weather, traffic and social media, while providers are mainly the interconnected devices, services and individuals in the Internet of Things (IoT). The continually increasing volume and variety of such data require timely integration, management and analysis - yet to be presented in a way that end-users can easily understand. Data that interact in real-time preserve their value and enable a more in-depth understanding of real-world choices. This survey explored Open Data and reviewed twelve data observatories, focusing on their data management approaches. We investigated the observatories aims, designs and data types for some applied domains- namely transport, energy, environment, and social sensing. In what follows, we outlined five research challenges that influence their implementation

    The Role of Cross-Domain Use Cases in IoT – A Case Analysis

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    The strong rise of Internet connected things contrib- utes to the big data phenomenon. Data is increasingly becoming available from various devices in large vol- ume, variety and velocity. However, many Internet of Things (IoT) applications still seem to be used to solve single problems and infrastructural elements (e.g. sen- sors or data) are not openly available for other use cases. This paper argues that a growing need for cross- domain applications is approaching within the sharing economy and other ecosystem-based business models. It investigates the current state of such cross-domain ap- plications by analyzing currently deployed IoT case studies. The analysis shows that most cases remain in the silo mentality and focus on a specific domain. Only a few cases feature some cross-domain characteristics. These are especially present in smart city cases, which engage multiple stakeholders and promote the share of resources

    Big Data and Artificial Intelligence in Digital Finance

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    This open access book presents how cutting-edge digital technologies like Big Data, Machine Learning, Artificial Intelligence (AI), and Blockchain are set to disrupt the financial sector. The book illustrates how recent advances in these technologies facilitate banks, FinTech, and financial institutions to collect, process, analyze, and fully leverage the very large amounts of data that are nowadays produced and exchanged in the sector. To this end, the book also describes some more the most popular Big Data, AI and Blockchain applications in the sector, including novel applications in the areas of Know Your Customer (KYC), Personalized Wealth Management and Asset Management, Portfolio Risk Assessment, as well as variety of novel Usage-based Insurance applications based on Internet-of-Things data. Most of the presented applications have been developed, deployed and validated in real-life digital finance settings in the context of the European Commission funded INFINITECH project, which is a flagship innovation initiative for Big Data and AI in digital finance. This book is ideal for researchers and practitioners in Big Data, AI, banking and digital finance

    Smartphone as an Edge for Context-Aware Real-Time Processing for Personal e-Health

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    The medical domain is facing an ongoing challenge of how patients can share their health information and timeline with healthcare providers. This involves secure sharing, diverse data types, and formats reported by healthcare-related devices. A multilayer framework can address these challenges in the context of the Internet of Medical Things (IoMT). This framework utilizes smartphone sensors, external services, and medical devices that measure vital signs and communicate such real-time data with smartphones. The smartphone serves as an “edge device” to visualize, analyze, store, and report context- aware data to the cloud layer. Focusing on medical device connectivity, mobile security, data collection, and interoperability for frictionless data processing allows for building context-aware personal medical records (PMRs). These PMRs are then securely transmitted through a communication protocol, Message Queuing Telemetry Transport (MQTT), to be then utilized by authorized medical staff and healthcare institutions. MQTT is a lightweight, intuitive, and easy-to-use messaging protocol suitable for IoMT systems. Consequently, these PMRs are to be further processed in a cloud computing platform, Amazon Web Services (AWS). Through AWS and its services, architecting a customized data pipeline from the mobile user to the cloud allows displaying of useful analytics to healthcare stakeholders, secure storage, and SMS notifications. Our results demonstrate that this framework preserves the patient’s health-related timeline and shares this information with professionals. Through a serverless Business intelligence interactive dashboard generated from AWS QuickSight, further querying and data filtering techniques are applied to the PMRs which identify key metrics and trends

    Big Data and Artificial Intelligence in Digital Finance

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    This open access book presents how cutting-edge digital technologies like Big Data, Machine Learning, Artificial Intelligence (AI), and Blockchain are set to disrupt the financial sector. The book illustrates how recent advances in these technologies facilitate banks, FinTech, and financial institutions to collect, process, analyze, and fully leverage the very large amounts of data that are nowadays produced and exchanged in the sector. To this end, the book also describes some more the most popular Big Data, AI and Blockchain applications in the sector, including novel applications in the areas of Know Your Customer (KYC), Personalized Wealth Management and Asset Management, Portfolio Risk Assessment, as well as variety of novel Usage-based Insurance applications based on Internet-of-Things data. Most of the presented applications have been developed, deployed and validated in real-life digital finance settings in the context of the European Commission funded INFINITECH project, which is a flagship innovation initiative for Big Data and AI in digital finance. This book is ideal for researchers and practitioners in Big Data, AI, banking and digital finance

    FAIR4PGHD: A framework for FAIR implementation over PGHD

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    Patient Generated Health Data (PGHD) are being considered for integration with health facilities, however little is known about how such data can be made machine-actionable in a way that meets FAIR guidelines. This article proposes a 5-stage framework that can be used to achieve this

    Multimodal Wearable Intelligence for Dementia Care in Healthcare 4.0: A Survey

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    As a new revolution of Ubiquitous Computing and Internet of Things, multimodal wearable intelligence technique is rapidly becoming a new research topic in both academic and industrial fields. Owning to the rapid spread of wearable and mobile devices, this technique is evolving healthcare from traditional hub-based systems to more personalised healthcare systems. This trend is well-aligned with recent Healthcare 4.0 which is a continuous process of transforming the entire healthcare value chain to be preventive, precise, predictive and personalised, with significant benefits to elder care. But empowering the utility of multimodal wearable intelligence technique for elderly care like people with dementia is significantly challenging considering many issues, such as shortage of cost-effective wearable sensors, heterogeneity of wearable devices connected, high demand for interoperability, etc. Focusing on these challenges, this paper gives a systematic review of advanced multimodal wearable intelligence technologies for dementia care in Healthcare 4.0. One framework is proposed for reviewing the current research of wearable intelligence, and key enabling technologies, major applications, and successful case studies in dementia care, and finally points out future research trends and challenges in Healthcare 4.0
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