Web2Touch 2019: Semantic Technologies for Smart Information Sharing and Web Collaboration

This foreword introduces a summary of themes and papers of the Web2Touch (W2T) 2019 Track at the 28th IEEE WETICE Conference held in Capri, June 2019. W2T 2019 includes ten full papers and one short paper. They all address relevant issues in the field of information sharing for collaboration, including, big data analytics, knowledge engineering, linked open data, applications of smart Web technologies, and smart care. The papers are a portfolio of hot issues in research and applications of semantics, smart technologies (e.g., IoT, sensors, devices for tele-monitoring, and smart contents management) with crucial topics, such as big data analysis, knowledge representation, smart enterprise management, among the others. This track shows how cooperative technologies based on knowledge representation, intelligent tools, and enhanced Web engineering can enhance collaborative work through smart service design and delivery, so it contributes to radically change the role of the semantic Web and applications

Improving Interaction in Integrated Chronic Care Management

Health and social care services are under increasing pressure to come up with adequate solutions to manage the demand and supply equation. Integrated care is one way to deal with this wicked problem, but new approaches and service implementation strategies are necessary to realize its full value and quality of outcome. Focusing on the four relevant \u2018blocks of interaction\u2019 identified by Prahalad and Ramaswamy, the paper examines the key role of information and communication technology (ICT) in facilitating the integrated care effort. It then develops those insights into a set of DART-informed guiding principles of practical use to decision-makers and IS/IT developers in the design of resource integration mechanisms for the management of chronic care settings. The paper uses home care services as a blueprint

reusing analysis schemas in odb applications a chart based approach

This paper presents a method for creating, indexing and reusing analysis schemas in developing Object-oriented Data-base (ODB) applications. Analysis schemas are specified by using analysis charts, a user-oriented set of forms structured according to the TQL++ Object-oriented specification model, and are classified according to their structural characteristics and content. A set of analysis charts forms a reusable schema, referred to as an analysis stack. The developer can retrieve and examine stacks by accessing analysis charts containing relevant entity names and structures. Charts are connected by links reproducing TQL++ relationships and connecting 'similar' schemas. The paper presents the measures of similarity between charts and describes the organization of charts in a reuse repository. A Thesaurus of relevant terms and synonyms is coupled with the repository. The Thesaurus and the repository are the basis for guiding developers in deriving new ODB applications through a sequence of steps proposed by a CHarting and Analysis for Reuse Tool (CHART). The methodology for reusing analysis schemas, based on navigation in the repository, and the support tool are described

SEEMP: A marketplace for the Labour Market

Employment Services are an important topic in the agenda of local governments and in the EU due to their social implications, such as sustainability, workforce mobility, workers’ re-qualification paths, training for fresh graduates and students. Many administrations started their own E-Government projects whose imitations emerge as the demand of workers mobility increases. The SEEMP system presented in this paper overcomes this issue in different ways: starting bilateral communications with near-border similar offices, building a federation of the local employment services, and merging isolate trials. The SEEMP approach relies on a distributed semantic service oriented infrastructure able to federate local projects, in order to create geographically aggregated services for employment by leveraging existing local ones. The social and technical aspects of the SEEMP project are presented, showing how the SEEMP system is integrated with National level systems

The SEEMP Approach to Semantic Interoperability for E-Employment

SEEMP is a European Project that promotes increased partnership between labour market actors and the development of closer relations between private and public employment services, making optimal use of the various actors’ specific characteristics, thus providing job-seekers and employers with better services. The need for a flexible collaboration gives rise to the issue of interoperability in both data exchange and share of services. SEEMP proposes a solution that relies on the concepts of services and semantics in order to provide a meaningful service-based communication among labour market actors requiring a minimal shared commitment

Workingage: providing occupational safety through pervasive Sensing and data driven behavior modeling

The aging of the working population calls for innovative approaches to monitor and support the changes of physical,physiological and psycho-social capabilities of workers over time, as well as to promote habits aimed at improving both health and productivity. This paper presents the WorkingAge (Smart Working Environments for All Ages - WA)project, which focuses on innovative Human Computer Interaction methods (such as augmented and/or virtual reality or gesture/voice/noise recognition or gaze tracking) to improve the users’ psychological/emotional/health state at workplaces. Based on Internet of Things (IoT) technologies and on data driven models of the users’ characteristics and behaviors, the WA Tool will monitor the state of users to automatically provide a set of suggestions promoting healthy habits in their working environment as well as in their daily living activities. The paper presents at the basis of the WA project, its hardware-software sensor architecture, and the elements of the ontology capturing the key concepts of the data collected to profile the users

Gesture Recognition in an IoT environment: a Machine Learning-based Prototype

The spread of IoT and wearable devices is bringing out gesture interfaces as a solution for a more natural and immediate human-machine interaction. The "Seamless" project is an industrial research and development experience, aimed to build a virtual environment where data collected by IoT sensors can be navigated through a gesture interface and virtual reality tools. This paper presents the portion of the project concerning gesture analysis, focused on the problem of automatically understanding a set of hand gestures, in order to give commands through a wearable control device. The tackled issue is to build a real time gesture recognition system based on inertial data, that can easily adapt to different users and to an extensible set of gestures. This gesture data variability is addressed by means of a supervised Machine Learning approach, that allows adapting the system response to different gestures and to various ways of performing them by different people. A context-aware adapter allows interfacing the gesture recognition system to various applications