CAPAS: A context-aware system architecture for physical activities monitoring

Attribute grammars are widely used by compiler-generators since it allows complete specifications of static semantics. They can also be applied to other fields of research, for instance, to human activities recognition. This paper aims to present CAPAS, a Context-aware system Architecture to monitor Physical ActivitieS. One of the components that is present in the architecture is the attribute grammar which is filled after the prediction is made according to the data gathered from the user through the sensors. According to some predefined rules, the physical activity is validated after an analysis on the attribute grammar, if it meets those requirements. Besides that it proposes an attribute grammar itself which should be able to be incorporated in a system in order to validate the performed physical activity.This work has been supported by FCT – Fundação˜ para a Ciência e Tecnologia within the Project Scope: ˆ UID/CEC/00319/2019

Context-aware Approach for Determining the Threshold Price in Name-Your-Own-Price Channels

Key feature of a context-aware application is the ability to adapt based on the change of context. Two approaches that are widely used in this regard are the context-action pair mapping where developers match an action to execute for a particular context change and the adaptive learning where a context-aware application refines its action over time based on the preceding action’s outcome. Both these approaches have limitation which makes them unsuitable in situations where a context-aware application has to deal with unknown context changes. In this paper we propose a framework where adaptation is carried out via concurrent multi-action evaluation of a dynamically created action space. This dynamic creation of the action space eliminates the need for relying on the developers to create context-action pairs and the concurrent multi-action evaluation reduces the adaptation time as opposed to the iterative approach used by adaptive learning techniques. Using our reference implementation of the framework we show how it could be used to dynamically determine the threshold price in an e-commerce system which uses the name-your-own-price (NYOP) strategy

Perspectives on engineering more usable context-aware systems

The expectations of the abilities of context-aware systems (C-AS) often differ from reality. It becomes difficult to program contextual services that react adequately to the circumstantial needs of users as developers need to know, beforehand: the set of contextual states that may exist, what information could accurately determine a contextual state within that set, and what appropriate action should be taken in that particular state. Although there exist many frameworks and tools which support the design and implementation of C-AS, there is less conceptual help for developers to inform them of what contextual situations and services are appropriate (or feasible) to be implemented. This report reviews the state-of-the-art conceptualisation of context, which is more focused on the representational interpretation of the concept, to introduce a perspective that also acknowledges its interactional interpretation. A combination of revised and new definitions is introduced, which give key insights for the development of more useful C-AS. By acknowledging situations as a dynamic phenomenon that arises from action (interaction), and needs to be understood by the developers, it facilitates the analysis of these subjective interpretations into programming constructs (representation). The conceptualisation is also complemented with a set of guidelines for developers, an illustration of their usage, and a further discussion on the future directions for the engineering of more usable C-AS. The introduced conceptualisation is targeted towards the creation of an open-source tool supported framework for the engineering of C-AS

Towards Efficient and Scalable Data-Intensive Content Delivery: State-of-the-Art, Issues and Challenges

This chapter presents the authors’ work for the Case Study entitled “Delivering Social Media with Scalability” within the framework of High-Performance Modelling and Simulation for Big Data Applications (cHiPSet) COST Action 1406. We identify some core research areas and give an outline of the publications we came up within the framework of the aforementioned action. The ease of user content generation within social media platforms, e.g. check-in information, multimedia data, etc., along with the proliferation of Global Positioning System (GPS)-enabled, always-connected capture devices lead to data streams of unprecedented amount and a radical change in information sharing. Social data streams raise a variety of practical challenges: derivation of real-time meaningful insights from effectively gathered social information, a paradigm shift for content distribution with the leverage of contextual data associated with user preferences, geographical characteristics and devices in general, etc. In this article we present the methodology we followed, the results of our work and the outline of a comprehensive survey, that depicts the state-of-the-art situation and organizes challenges concerning social media streams and the infrastructure of the data centers supporting the efficient access to data streams in terms of content distribution, data diffusion, data replication, energy efficiency and network infrastructure. The challenges of enabling better provisioning of social media data have been identified and they were based on the context of users accessing these resources. The existing literature has been systematized and the main research points and industrial efforts in the area were identified and analyzed. In our works, in the framework of the Action, we came up with potential solutions addressing the problems of the area and described how these fit in the general ecosystem