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Objectives: Current research aims to address the challenges of exchanging healthcare information, since when this information has to be shared, this happens by specifically designed medical applications or even by the patients themselves. Among the problems that the Health Information Exchange (HIE) initiative is facing are that (i) third party health data cannot be accessed without internet, (ii) there exist crucial delays in accessing citizens' data, (iii) the direct HIE can only happen among Healthcare Institutions. Methods: Towards the solution of these issues, a Device-to-Device (D2D) protocol has been specified, running on top of the Bluetooth protocol for efficient data exchange. This research is focused on this D2D protocol, by comparing the different Bluetooth profiles that can be used for transmitting this data, based on specific metrics considering the probabilities of transferring erroneous data. Findings: An evaluation of three Bluetooth profiles takes place, concluding that two of the three profiles must be used to respect the D2D protocol nature and be fully supported by the main market vendors' operating systems. Novelty:Based on this evaluation, the specified D2D protocol has been built on top of state-of-the-art short-range distance communication technologies, fully supporting the healthcare ecosystem towards the HIE paradigm. Doi: 10.28991/esj-2021-01276 Full Text: PDF

Product recommendation is considered a well-known technique for bringing customers and products together. With applications in music, electronic shops, or almost any platform the user daily deals with, the recommendation system's sole scope is to help customers and attract new ones to discover new products. Through product recommendation, transaction costs can also be decreased, improving overall decision-making and quality. To perform recommendations, a recommendation system must utilize customer feedback, such as habits, interests, prior transactions as well as information used in customer profiling, and finally deliver suggestions. Hence, data is the key factor in choosing the appropriate recommendation method and drawing specific suggestions. This research investigates the data challenges of recommendation systems, specifying collaborative-based, content-based, and hybrid-based recommendations. In this context, collaborative filtering is being explored, with the Surprise library and LightFM embeddings being analysed and compared on top of foodservice transactional data. The involved algorithms' metrics are being identified and parameterized, while hyperparameters are being tuned properly on top of this transactional data, concluding that LightFM provides more efficient recommendation results following the evaluation's precision and recall outcomes. Nevertheless, even though the Surprise library outperforms, it should be used when constructing user-friendly models, requiring low code and low technicalities. Doi: 10.28991/ESJ-2023-07-01-01 Full Text: PDF

The healthcare sector has been moving toward Electronic Health Record (EHR) systems that produce enormous amounts of healthcare data due to the increased emphasis on getting the appropriate information to the right person, wherever they are, at any time. This highlights the need for a holistic approach to ingest, exploit, and manage these huge amounts of data for achieving better health management and promotion in general. This manuscript proposes such an approach, providing a mechanism allowing all health ecosystem entities to obtain actionable knowledge from heterogeneous data in a multimodal way. The mechanism includes diverse techniques for automatically ingesting healthcare-related information from heterogeneous sources that produce batch/streaming data, managing, fusing, and aggregating this data into new data structures (i.e., Holistic Health Records (HHRs)). The latter enable the aggregation of data coming from different sources, such as Internet of Medical Things (IoMT) devices, online/offline platforms, while to effectively construct the HHRs, the mechanism develops various data management techniques covering the overall data path, from data acquisition and cleaning to data integration, modelling, and interpretation. The mechanism has been evaluated upon different healthcare scenarios, ranging from hospital-retrieved data to patient platforms, combined with data obtained from IoMT devices, having produced useful insights towards its successful and wide adaptation in this domain. In order to implement a paradigm shift from heterogeneous and independent data sources, limited data exploitation, and health records, the mechanism has combined multidisciplinary technologies toward HHRs. Doi: 10.28991/ESJ-2023-07-02-03 Full Text: PDF

The Healthcare 4.0 era is surrounded by challenges varying from the Internet of Medical Things (IoMT) devices' data collection, integration and interpretation. Several techniques have been developed that however do not propose solutions that can be applied to different scenarios or domains. When dealing with healthcare data, based on the severity and the application of their results, they should be provided almost in real-time, without any errors, inconsistencies or misunderstandings. Henceforth, in this manuscript a platform is proposed for efficiently managing healthcare data, by taking advantage of the latest techniques in Data Acquisition, 5G Network Slicing and Data Interoperability. In this platform, IoMT devices' data and network specifications can be acquired and segmented in different 5G network slices according to the severity and the computation requirements of different medical scenarios. In sequel, transformations are performed on the data of each network slice to address data heterogeneity issues, and provide the data of the same network slices into HL7 FHIR-compliant format, for further analysis.

Social networking apps, sites and technologies offer a wide range of opportunities for businesses and developers to exploit the vast amount of information and user-generated content produced through social networking. In addition, the notion of second screen TV usage appears more influential than ever, with viewers continuously seeking further information and deeper engagement while watching their favourite movies or TV shows. In this work, the authors present SAM, an innovative platform that combines social media, content syndication and targets second screen usage to enhance media content provisioning, renovate the interaction with end-users and enrich their experience. SAM incorporates modern technologies and novel features in the areas of content management, dynamic social media, social mining, semantic annotation and multi-device representation to facilitate an advanced business environment for broadcasters, content and metadata providers, and editors to better exploit their assets and increase their revenues.

Via four strategically designed pilot use cases coordinated in Bulgaria, Italy, Spain and the United Kingdom, PolicyCLOUD is delivering a unique, integrated environment of curated datasets and data manipulation and analysis tools of fundamental importance to stakeholders across Europe. The aim of Policy Cloud is to harness the potential of digitisation, big data, and cloud technologies to improve the modelling, creation, and implementation of policy. The digitisation of the global economy and society affects all sectors, is at the heart of the EU's political agenda and is necessary if we are to maintain our competitiveness. Having common ICT standards is one of the measures needed to ensure that European industries are at the forefront of developing and exploiting ICT technologies: they ensure interoperability and guarantee that such technologies work smoothly and reliably together. This will become increasingly important as in the future many more devices will be connected to each other, as defined in the EC Rolling Plan for ICT Standardisation. The European Commission proposes to focus standard-setting resources and communities on five priority areas: 5G, Internet of Things, cloud computing, cybersecurity, and data technologies, all essential for wider EU competitiveness, as defined in its 2021 Rolling Plan for ICT Standardisation1 PolicyCLOUD has defined a dedicated task on standardisation, contributing to European ICT standardisation. After a State-of-the-Art Landscape analysis of standardisation in the Context of EU policymaking in the ICT fields related to the PolicyCLOUD project. The document proceeds with the mapping of partners' use of standards related to their work in PolicyCLOUD, their engagement in relevant Standards Developments Organisations or related initiatives. This Standardisation Plan and Activities document details the specific activities to be implemented and the stakeholder groups to be targeted to foster the PolicyCLOUD alignment with global standards. Standards and Open-Source ...

AbstractWe present PolicyCLOUD: a prototype for an extensible serverless cloud-based system that supports evidence-based elaboration and analysis of policies. PolicyCLOUD allows flexible exploitation and management of policy-relevant dataflows, by enabling the practitioner to register datasets and specify a sequence of transformations and/or information extraction through registered ingest functions. Once a possibly transformed dataset has been ingested, additional insights can be retrieved by further applying registered analytic functions to it. PolicyCLOUD was built as an extensible framework toward the creation of an analytic ecosystem. As of now, we have developed several essential ingest and analytic functions that are built-in within the framework. They include data cleaning, enhanced interoperability, and sentiment analysis generic functions; in addition, a trend analysis function is being created as a new built-in function. PolicyCLOUD has also the ability to tap on the analytic capabilities of external tools; we demonstrate this with a social dynamics tool implemented in conjunction with PolicyCLOUD, and describe how this stand-alone tool can be integrated with the PolicyCLOUD platform to enrich it with policy modeling, design and simulation capabilities. Furthermore, PolicyCLOUD is supported by a tailor-made legal and ethical framework derived from privacy/data protection best practices and existing standards at the EU level, which regulates the usage and dissemination of datasets and analytic functions throughout its policy-relevant dataflows. The article describes and evaluates the application of PolicyCLOUD to four families of pilots that cover a wide range of policy scenarios.