A novel healthcare monitoring framework based on the cloud environment and a big data analytics engine is proposed to precisely store and analyze healthcare data, and to improve the classification accuracy

The purpose of this research is to identify the level of digital exclusion and also to ascertain the reasons behind the marginalization. Further, the research is also focussing on understanding the noteworthy perceptions that will be effective in creating and implementing strategies for digital inclusion and engagement of the elderly in India.

This research proposes to set up an experiment in order to measure the effect a digital platform has on the capabilities of elderly to live at home. The results show that a digital platform will in fact increase capabilities of elderly.

Online Glossary about Analytic Quality

There are concerns at an EU level over the role of online platforms, which are understood by the European Commission as software-based facilities offering multi-sided markets where providers and users of content, goods and services can meet. In response to the EU-level interest in this issue, BIS has conducted internal research addressing the market issues around platforms and come to a number of hypotheses about how dynamic competition affects online platforms. This research considers a range of quantitative and qualitative evidence around those hypotheses in five case study sectors.

The book describes the principles of IT architecture to develop the creation of an information model of business strategic requirements. Highlighting the importance of organizational goals within a business, this book is an essential read for employees on a managerial and executive level who are involved in the organizational development of a company.

Nowadays, there are plenty of sensing devices that enable the measurement of physiological, environmental, and behavioral parameters of people 24 hours a day, seven days a week and provide huge quantities of different data. Data and signals coming from sensing devices, installed in indoor or outdoor environments or often worn by the users, generate heterogeneous and complex structured datasets, most of the time not uniformly structured. The artificial intelligence (AI) algorithms applied to these sets of data have demonstrated capabilities to infer indices related to a subject's status and well-being [1]. Well-being is a key parameter in the World Health Organization (WHO) definition of health, considering its physical, mental, and social spheres. Quantitatively assessing a subject's well-being is of paramount importance if we want to assess the whole status of a person, which is particularly useful in the case of ageing people living alone. Assessment allows for continuous remote monitoring to improve people's quality of life (QoL) according to their perceptions, needs, and preferences. Technology undoubtedly plays a pivotal role in this regard, providing us new tools to support the objective evaluation of a subject's status, including her/his perception of the living environment. Its potential is huge, also in terms of support to the healthcare system and ageing people; however, there are several engineering challenges to consider, especially in terms of sensors integrability, connectivity, and metrological performance, in order to obtain reliable and accurate measurement systems.

General managers and chief executive officers generally determine the information they need through four basic approaches: the by-product technique, the null approach, the key indicator system, and the total study process. A new approach, the Critical Success Factors (CFS) Method, focuses on individual managers and on each manager's current hard and soft information needs. The four prime sources of critical success factors are the structure of the particular industry; the competitive strategy, industry position, and geographic location; environmental factors; and temporal factors. The CFS method is useful at each level of general management.