A survey of context modelling and reasoning techniques

Nowadays, numerous journals and conferences have published articles related to context-aware systems, indicating many researchers' interest. Therefore, the goal of this paper is to review the works that were published in journals, suggest a new classification framework of context-aware systems, and explore each feature of classification framework. This paper is based on a literature review of context-aware systems from 2000 to 2007 using a keyword index and article title search. The classification framework is developed based on the architecture of context-aware systems, which consists of the following five layers: concept and research layer, network layer, middleware layer, application layer and user infrastructure layer. The articles are categorized based on the classification framework. This paper allows researchers to extract several lessons learned that are important for the implementation of context-aware systems.

Context-aware systems

Recommender systems have been researched extensively by the Technology Enhanced Learning (TEL) community during the last decade. By identifying suitable resources from a potentially overwhelming variety of choices, such systems offer a promising approach to facilitate both learning and teaching tasks. As learning is taking place in extremely diverse and rich environments, the incorporation of contextual information about the user in the recommendation process has attracted major interest. Such contextualization is researched as a paradigm for building intelligent systems that can better predict and anticipate the needs of users, and act more efficiently in response to their behavior. In this paper, we try to assess the degree to which current work in TEL recommender systems has achieved this, as well as outline areas in which further work is needed. First, we present a context framework that identifies relevant context dimensions for TEL applications. Then, we present an analysis of existing TEL recommender systems along these dimensions. Finally, based on our survey results, we outline topics on which further research is needed.

Context-Aware Recommender Systems for Learning: A Survey and Future Challenges

Building a good content adaptation service for mobile devices poses many challenges. To meet these challenges, this quality-of-service-aware decision engine automatically negotiates for the appropriate adaptation decision for synthesizing an optimal content version.

/pdf/a-context-aware-decision-engine-for-content-adaptation-1aogs58hzu.pdf

A context-aware decision engine for content adaptation

During an online event, a spectator process monitors a state of the event, updating a spectator model, so that spectator data streams can be generated and provided to spectators. The spectator data streams can be formatted and provided with content appropriate for use by different types of spectator devices used by the spectators. The spectator process can also automatically generate virtual commentary appropriate for the action occurring in the event for inclusion in the spectator data streams. A media server receives the rendered data streams and distributes them to the electronic devices being used by the spectators. The distribution can be delayed to avoid a spectator conveying information to a participant that would provide an unfair advantage. Executable code can be included in the spectator data stream to provide additional functionality and facilitate interaction between the spectators, and to enable a spectator to also “play” the game.

Online gaming spectator system

We address the challenges of building a good content adaptation service for mobile devices and propose a decision engine that is user-centric with QoS awareness, which can automatically negotiate for the appropriate adaptation decision to use in the synthesis of an optimal adapted version. The QoS-sensitive approach complements the lossy nature of the transcoding operations. The decision engine will look for the best trade off among various parameters in order to reduce the loss of quality in various domains. Quantitative methods are suggested to measure the QoS of the content versions in various quality domains. Based on the particular user perception and other contextual information on the client capability, the network connection, and the requested content, the proposed negotiation algorithm will determine a content version with a good aggregate score. We have built a prototype document adaptation system for PDF documents to demonstrate the viability of our approach.

/pdf/user-centric-content-negotiation-for-effective-adaptation-3wddj8xmqi.pdf

User-centric content negotiation for effective adaptation service in mobile computing

We propose a method that can find the optimal tradeoff point between transcoding overhead (CPU cost) and storage needed for the various pre-processed content variants (I/O cost). The method selectively pre-adapts a subset of content variants and leaves the generation of the residue to dynamic content adaptation with this pre-adapted subset as an input. We prove bounds regarding the optimality of the algorithm employed. The proposed model creates a collaborative environment across the components of client, proxy and server, based on which we study the distribution of adaptation complexity across these components. We use simulation to verify the projected benefits. The method has been successfully implemented in a trial PDF document content adaptation system.

/pdf/on-balancing-between-transcoding-overhead-and-spatial-2kwtq1wcfj.pdf

On balancing between transcoding overhead and spatial consumption in content adaptation

We propose a decision engine with QoS awareness that can automatically negotiate for the appropriate adaptation strategies to be used to produce an optimal adapted version. The QoS-sensitive approach complements the lossy nature of the transcoding operations. The decision engine will look for the best tradeoff among various parameters in order to reduce the loss of quality, in various domains. Quantitative methods are suggested to measure the QoS of the content versions. Based on the particular user perception of these quality domains and other context information on the client capability, the proposed negotiation algorithm will determine a content version with a good aggregate score. We study factors such as processing overhead and the optimization accuracy of the algorithm, and their tradeoff. We built a prototype document adaptation system for PDF documents to demonstrate the viability of our approach.

A QoS-sensitive content adaptation system for mobile computing

Content adaptation is a crucial step in making desktop-oriented Web resources available to mobile, small device users. In this paper, we propose a decision engine comprising a content analysis module and a negotiation module to serve as the core of a content adaptation architecture. The content analysis module parses a structured Web document originally intended for the desktop into small sections and transforms the document into a form that is best suited for rendering in a constrained mobile device. The transformation also provides the user with the best content value in an adapted Web page while preserving content integrity. With the transformed document, the negotiation module selects the best rendering parameters to be used in the synthesis of an optimal adapted version of the content. The decisions made are based on the user's preference and QoS considerations. We have built a prototype to demonstrate the viability of our approach.

/pdf/user-centric-adaptation-of-structured-web-documents-for-gw8p670lbt.pdf

Wai Yip Lum

Papers

A context-aware decision engine for content adaptation

User-centric content negotiation for effective adaptation service in mobile computing

On balancing between transcoding overhead and spatial consumption in content adaptation

A QoS-sensitive content adaptation system for mobile computing

User-centric adaptation of structured Web documents for small devices