Edge and cluster computing as enabling infrastructure for Internet of Medical Things

(c) 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.The continuous adoption of fitness and medical smart sensors are boosting the development of Internet of Medical Things (IoMT), reshaping and revolutionizing Healthcare. This digital transformation is paving the way to new forms of care based on real-time analysis of huge amounts of data produced by sensors, which is seen as a basis for improving clinical efficiency and helping to save lives. A medical sensor typically produces several KBs of data per second so the collection and analysis of these data can be approached with Big Data technologies. The aim of this paper is to present and evaluate a hybrid architecture for real-time anomaly detection from data streams coming from sensors attached to patients. The architecture includes an edge computing data staging platform based on Raspberry Pi 3 for data logging, data transformation in RDF triple and data streaming towards a cluster computing running Apache Kafka for collecting RDFStreams, Apache Flink for running a parallel version of the Hierarchical Temporal Memory algorithm and Cassandra for data storing. The different layers of the architecture have been evaluated in terms of both CPU performance and memory usage using the REALDISP dataset.Peer ReviewedPostprint (author's final draft

IoT and semantic web technologies for event detection in natural disasters

This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.Natural disasters cannot be predicted well in advance, but it is still possible to decrease the loss of life and mitigate the damages, exploiting some peculiarities that distinguish them. Smart collection, integration, and analysis of data produced by distributed sensors and services are key elements for understanding the context and supporting decision making process for disaster prevention and management. In this paper, we demonstrate how Internet of Things and Semantic Web technologies can be effectively used for abnormal event detection in the contest of an earthquake. In our proposal, a prototype system, which retrieves the data streams from IoT sensors and web services, is presented. In order to contextualize and give a meaning to the data, semantic web technologies are applied for data annotation. We evaluate our system performances by measuring the response time and other parameters that are important in a disaster detection scenario.Peer ReviewedPostprint (author's final draft

Environments to support collaborative software engineering

With increasing globalisation of software production, widespread use of software components, and the need to maintain software systems over long periods of time, there has been a recognition that better support for collaborative working is needed by software engineers. In this paper, two approaches to developing improved system support for collaborative software engineering are described: GENESIS and OPHELIA. As both projects are moving towards industrial trials and eventual publicreleases of their systems, this exercise of comparing and contrasting our approaches has provided the basis for future collaboration between our projects particularly in carrying out comparative studies of our approaches in practical use

Enabling technologies for future learning scenarios: the semantic grid for human learning

In this paper, starting from the limitations and constrains of traditional human learning approaches, we outline new suitable approaches to education and training in future knowledge based society. In our vision, learning and teaching are no longer standalone activities but complex, conversational and experiential-based processes implying collaboration, direct experience, mutual trust and shared interests. We identify characteristics of the environments suitable for these processes, and we compare different enabling technology infrastructures in order to justify why the Semantic Grid for Human Learning, that is a particular enhanced instance of the traditional Semantic Grid, is the most appropriate infrastructure to build our vision on. Finally, we present a realistic learning scenario as a case study, proving the effectiveness of our innovative learning approachesforfuture Education and Training

Environments to Support Collaborative Software Engineering

With increasing globalisation of software production, widespread use of software components, and the need to maintain software systems over long periods of time, there has been a recognition that better support for collaborative working is needed by software engineers. In this paper, two approaches to developing improved system support for collaborative software engineering are described: GENESIS and OPHELIA. As both project are moving towards industrial trials and eventual public releases of their systems, this exercise of comparing and contrasting our approaches has provided the basis for future collaboration between our projects particularly in carrying out comparative studies of our approaches in practical use

Supporting learning object repository by automatic extraction of metadata

The Learning Objects Repositories are electronic databases able to deliver material on the web allowing instructors sharing and reusing educational units and students accessing and enjoying them. The best way to guarantee these interactions is a good indexing. Each content needs a machine-understandable description able to declare requirements and limits for its right use and to improve any research and delivery action. These descriptions are stored in the metadata. Filling in the metadata is a boring and time-consuming activity but it is very important since it could influence the choice of the best material to deliver. This paper describes a possible methodological approach to automate this activity by extracting metadata directly from the files setting up the learning object itself. In the literature, there are many methods able to automatically characterize the technological aspects of the content, but very few of them are able to provide information about its pedagogical features. The proposed approach tries to draw together information theory, learning models, statistical analysis and ad hoc heuristics to extract a wide set of fields of the metadata. The results of a first experimentation are particularly encouraging to think about this approach as a solution to support learning object repositories and other platforms having needs to manage wide content storage and huge amount of users with various personal features, devices for interaction and goals as in the MOOCs. © 2015, Giunti. All rights reserved

Automatic extraction of metadata from learning objects

A good indexing of the learning objects is the better way to guarantee their reuse in the distance-learning context. We need to supply each content of a machine-understandable description including both technological and pedagogical information able to declare requirements and limits for its right use and to improve any research and delivery action. These descriptions are stored in the metadata: standard-based data structures. Filling in the metadata is a boring and time-consuming activity but it is very important since it could influence, in the learner-centered processes, the choice of the best material to deliver. This paper describes a possible methodological approach to automate this activity by extracting metadata directly from the files setting up the learning object itself. In the literature there are many methods able to automatically characterize the technological aspects of the content (format, dimensions, HW and SW requirements, etc.) but very few of them are able to provide information about its pedagogical features (educational style, semantic density, difficulty, time to learn, interactivity level, etc.). The proposed approach tries to draw together information theory, learning models, statistical analysis and ad hoc heuristics to extract a wide set of fields of the metadata. The results of a first experimentation are particularly encouraging to think about this approach as a solution to enrich content management systems and, in particular, e-learning platforms having needs to manage wide content storage and huge amount of users with various personal features, devices for interaction and goals as in the MOOCs. © 2014 IEEE

ELeGI as enabling architecture for e-learning 2.0

Web 2.0 is supposed to be the second generation of internet-based services – such as social networking sites, wikis, communication tools and folksonomies – that let people collaborate and share information online in a previously unavailable way. Currently, many leading enterprises have a strong interest in Web 2.0 and on the impact that it can have on traditional web-based applications such as e-learning, namely, e-learning 2.0. This paper analyses what the European Learning Grid Infrastructure (ELeGI) project could provide to e-learning 2.0, in terms of its vision of learning, processes, methodologies and technologies. Our preliminary investigations have raised up which results obtained in the ELeGI project can give support to e-learning 2.0 and improve some of its typical aspects and processes

Advanced Ontology Management System for Personalised e-Learning

The use of ontologies to model the knowledge of specific domains represents a key aspect for the integration of information coming from different sources, for supporting collaboration within virtual communities, for improving information retrieval, and more generally, it is important for reasoning on available knowledge. In the e-Learning field, ontologies can be used to model educational domains and to build, organize and update specific learning resources (i.e. learning objects, learner profiles, learning paths, etc.). One of the main problems of educational domains modeling is the lacking of expertise in the knowledge engineering field by the e-Learning actors. This paper presents an integrated approach to manage the life-cycle of ontologies, used to define personalised e-Learning experiences supporting blended learning activities, without any specific expertise in knowledge engineering

Implementing New Advanced Learning Scenarios Through GRID Technologies

In this paper we present our vision concerning the use of GRID technologies for the implementation of the next generation of learning solutions and in particular for supporting distance learning. We believe that the new requirements that new Learning environment should satisfy could transform these solutions into killing applications for GRID technologies. Our ideas move essentially from two main thoughtfulness: from one side there are the lacks characterising the existing software platforms for supporting traditional and distance learning processes and, from the other side there is the continuous achievement of GRID technologies as a solution for truly implementation of e-science infrastructure. The main drawbacks of current distance learning solutions are twofold: they only support a part of the whole learning process (the simple one), and how ICT is used