Towards personalised and adaptive QoS assessments via context awareness

Quality of Service (QoS ) properties play an important role in distinguishing between functionally-equivalent services and accommodating the different expectations of users. However, the subjective nature of some properties and the dynamic and unreliable nature of service environments may result in cases where the quality values advertised by the service provider are either missing or untrustworthy. To tackle this, a number of QoS estimation approaches have been proposed, utilising the observation history available on a service to predict its performance. Although the context underlying such previous observations (and corresponding to both user and service related factors) could provide an important source of information for the QoS estimation process, it has only been utilised to a limited extent by existing approaches. In response, we propose a context-aware quality learning model, realised via a learning-enabled service agent, exploiting the contextual characteristics of the domain in order to provide more personalised, accurate and relevant quality estimations for the situation at hand. The experiments conducted demonstrate the effectiveness of the proposed approach, showing promising results (in terms of prediction accuracy) in different types of changing service environments

A distributed service-based system for homecare self-management

Aging is becoming a critical issue for Europe and many countries around the world. It is imposing significant burden on societies and their national services. Enabling longer home independent living is seen one of the most promising ways to overcome this issue. However, to achieve a number of challenges need to be overcome, especially those related to management of health and disease let alone other social and logistical barriers. One of these challenges is malnutrition, which is considered one of the root causes for the occurrence of other diseases. This paper presents the design of a distributed system that enables homecare in the context of management of self-feeding through balanced nutritional intake. The design employs a service-based system that incorporates a number of services including monitoring of activities, nutritional reasoning for assessing feeding habits, diet recommending for food planning, and marketplace invocation for automating food shopping to meet dietary requirement