How will the Internet of Things enable Augmented Personalized Health?

Internet-of-Things (IoT) is profoundly redefining the way we create, consume, and share information. Health aficionados and citizens are increasingly using IoT technologies to track their sleep, food intake, activity, vital body signals, and other physiological observations. This is complemented by IoT systems that continuously collect health-related data from the environment and inside the living quarters. Together, these have created an opportunity for a new generation of healthcare solutions. However, interpreting data to understand an individual's health is challenging. It is usually necessary to look at that individual's clinical record and behavioral information, as well as social and environmental information affecting that individual. Interpreting how well a patient is doing also requires looking at his adherence to respective health objectives, application of relevant clinical knowledge and the desired outcomes. We resort to the vision of Augmented Personalized Healthcare (APH) to exploit the extensive variety of relevant data and medical knowledge using Artificial Intelligence (AI) techniques to extend and enhance human health to presents various stages of augmented health management strategies: self-monitoring, self-appraisal, self-management, intervention, and disease progress tracking and prediction. kHealth technology, a specific incarnation of APH, and its application to Asthma and other diseases are used to provide illustrations and discuss alternatives for technology-assisted health management. Several prominent efforts involving IoT and patient-generated health data (PGHD) with respect converting multimodal data into actionable information (big data to smart data) are also identified. Roles of three components in an evidence-based semantic perception approach- Contextualization, Abstraction, and Personalization are discussed

User's Privacy in Recommendation Systems Applying Online Social Network Data, A Survey and Taxonomy

Recommender systems have become an integral part of many social networks and extract knowledge from a user's personal and sensitive data both explicitly, with the user's knowledge, and implicitly. This trend has created major privacy concerns as users are mostly unaware of what data and how much data is being used and how securely it is used. In this context, several works have been done to address privacy concerns for usage in online social network data and by recommender systems. This paper surveys the main privacy concerns, measurements and privacy-preserving techniques used in large-scale online social networks and recommender systems. It is based on historical works on security, privacy-preserving, statistical modeling, and datasets to provide an overview of the technical difficulties and problems associated with privacy preserving in online social networks.Comment: 26 pages, IET book chapter on big data recommender system

Service-oriented coordination platform for technology-enhanced learning

It is currently difficult to coordinate learning processes, not only because multiple stakeholders are involved (such as students, teachers, administrative staff, technical staff), but also because these processes are driven by sophisticated rules (such as rules on how to provide learning material, rules on how to assess students’ progress, rules on how to share educational responsibilities). This is one of the reasons for the slow progress in technology-enhanced learning. Consequently, there is a clear demand for technological facilitation of the coordination of learning processes. In this work, we suggest some solution directions that are based on SOA (Service-Oriented Architecture). In particular, we propose a coordination service pattern consistent with SOA and based on requirements that follow from an analysis of both learning processes and potentially useful support technologies. We present the service pattern considering both functional and non-functional issues, and we address policy enforcement as well. Finally, we complement our proposed architecture-level solution directions with an example. The example illustrates our ideas and is also used to identify: (i) a short list of educational IT services; (ii) related non-functional concerns; they will be considered in future work

What should economists measure? The implications of mass production vs. mass customization

Consumer behavior ; Production (Economic theory) ; Productivity

An Ontology-based Approach to Web Site Design and Development

Building a data-intensive web site is a complex task. Ad-hoc rapid prototyping approaches easily lead to unsatisfactory results, e. g. poor maintainability and extensibility. The situation becomes even more difficult when customization issues arise and web sites need to present customized views to individual users. To address this problem, a number of model-based approaches have been proposed, which attempt to simplify the design and development of data-intensive web sites. However these approaches suffer a number of limitations, such as relatively little support for the composition of sophisticated user interfaces and the specification of presentation styles and little support for customization design. In this work we propose and implement an ontology-based approach, OntoWeaver, which provides comprehensive support for the design and development of data-intensive web sites. In particular, OntoWeaver provides a set of ontologies to represent all aspects of data-intensive web sites in a declarative and re-usable format. The declarative nature of the specification of web sites opens up a number of possibilities with respect to intelligent analysis and management. Moreover, OntoWeaver includes providing high level support for developing customized web sites. Finally, it offers a powerful tool suite to support the design and development of data-intensive web sites. In the course of this research, we have also extended OntoWeaver by addressing the issue of integrating web service technology into a high level web site design framework