APPLYING SITUATION-SERVICE FIT TO PHYSICAL ENVIRONMENTS ENHANCED BY UBIQUITOUS INFORMATION SYSTEMS

Ubiquitous Information Systems (UIS) embedded in everyday environments are required to provide means for supporting complex behavioral task requirements by users. With the concept of a situation, we propose a knowledge level that can be used by users for understanding UIS-enhanced environments. It is discussed how the concept of situation differs from the concept of context. Situations are used to identify supporting services. At the core of this paper, we present how the Situation-Service Fit construct is used in early design stages (study 1) and later during the prototype phase (study 2). In study 2 we introduce an instrument for three more detailed fit constructs, i.e., Behavior-Service Fit, Modality-Service Fit, and Spatial-Service Fit. By additional evaluation of constructs known from various technology acceptance theories, we provide an innovative instrument for investigating UIS during the whole design cycle

Quality assessment technique for ubiquitous software and middleware

The new paradigm of computing or information systems is ubiquitous computing systems. The technology-oriented issues of ubiquitous computing systems have made researchers pay much attention to the feasibility study of the technologies rather than building quality assurance indices or guidelines. In this context, measuring quality is the key to developing high-quality ubiquitous computing products. For this reason, various quality models have been defined, adopted and enhanced over the years, for example, the need for one recognised standard quality model (ISO/IEC 9126) is the result of a consensus for a software quality model on three levels: characteristics, sub-characteristics, and metrics. However, it is very much unlikely that this scheme will be directly applicable to ubiquitous computing environments which are considerably different to conventional software, trailing a big concern which is being given to reformulate existing methods, and especially to elaborate new assessment techniques for ubiquitous computing environments. This paper selects appropriate quality characteristics for the ubiquitous computing environment, which can be used as the quality target for both ubiquitous computing product evaluation processes ad development processes. Further, each of the quality characteristics has been expanded with evaluation questions and metrics, in some cases with measures. In addition, this quality model has been applied to the industrial setting of the ubiquitous computing environment. These have revealed that while the approach was sound, there are some parts to be more developed in the future

Context-driven progressive enhancement of mobile web applications: a multicriteria decision-making approach

Personal computing has become all about mobile and embedded devices. As a result, the adoption rate of smartphones is rapidly increasing and this trend has set a need for mobile applications to be available at anytime, anywhere and on any device. Despite the obvious advantages of such immersive mobile applications, software developers are increasingly facing the challenges related to device fragmentation. Current application development solutions are insufficiently prepared for handling the enormous variety of software platforms and hardware characteristics covering the mobile eco-system. As a result, maintaining a viable balance between development costs and market coverage has turned out to be a challenging issue when developing mobile applications. This article proposes a context-aware software platform for the development and delivery of self-adaptive mobile applications over the Web. An adaptive application composition approach is introduced, capable of autonomously bypassing context-related fragmentation issues. This goal is achieved by incorporating and validating the concept of fine-grained progressive application enhancements based on a multicriteria decision-making strategy

Alcuni abstract di articoli che trattano argomenti relativi all'eHealth

Non utile per esam

The design of smart educational environments

This paper discusses the key characteristics of smart learning and the main challenges to be overcome when designing smart educational environments to support personalisation. In order to integrate smart learning environments into the learning ecosystem and educational contexts, innovative uses and new pedagogical approaches need to be implemented to orchestrate formal and informal learning. This contribution describes the main characteristics of smart learning and smart learning environments and sustains the relevance of taking the participation of future users into account during the design process to increase knowledge of the design and the implementation of new pedagogical approaches in smart learning environments

Personalised privacy in pervasive and ubiquitous systems

Our world is edging closer to the realisation of pervasive systems and their integration in our everyday life. While pervasive systems are capable of offering many benefits for everyone, the amount and quality of personal information that becomes available raise concerns about maintaining user privacy and create a real need to reform existing privacy practices and provide appropriate safeguards for the user of pervasive environments. This thesis presents the PERSOnalised Negotiation, Identity Selection and Management (PersoNISM) system; a comprehensive approach to privacy protection in pervasive environments using context aware dynamic personalisation and behaviour learning. The aim of the PersoNISM system is twofold: to provide the user with a comprehensive set of privacy protecting tools and to help them make the best use of these tools according to their privacy needs. The PersoNISM system allows users to: a) configure the terms and conditions of data disclosure through the process of privacy policy negotiation, which addresses the current “take it or leave it” approach; b) use multiple identities to interact with pervasive services to avoid the accumulation of vast amounts of personal information in a single user profile; and c) selectively disclose information based on the type of information, who requests it, under what context, for what purpose and how the information will be treated. The PersoNISM system learns user privacy preferences by monitoring the behaviour of the user and uses them to personalise and/or automate the decision making processes in order to unburden the user from manually controlling these complex mechanisms. The PersoNISM system has been designed, implemented, demonstrated and evaluated during three EU funded projects