Collaborative Filtering and Inference Rules for Context-Aware Learning Object Recommendation

Learning objects strive for reusability in e-Learning to reduce cost and allow personalization of content. We argue that learning objects require adapted Information Retrieval systems. In the spirit of the Semantic Web, we discuss the semantic description, discovery, and composition of learning objects using Web-based MP3 objects as examples. As part of our project, we tag learning objects with both objective and subjective metadata. We study the application of collaborative filtering as prototyped in the RACOFI (Rule-Applying Collaborative Filtering) Composer system, which consists of two libraries and their associated engines: a collaborative filtering system and an inference rule system. We are currently developing RACOFI to generate context-aware recommendation lists. Context is handled by multidimensional predictions produced from a database-driven scalable collaborative filtering algorithm. Rules are then applied to the predictions to customize the recommendations according to user profiles. The prototype is available at inDiscover.net

Multi-device application middleware: leveraging the ubiquity of the Web with webinos

The broad range of connected devices has turned the Internet into a ubiquitous concept. In addition to desktop and laptop PCs, the Internet currently connects mobile devices, home entertainment systems, and even in-car units. From this ubiquitous evolution towards sensor-rich devices, the opportunity arises for various new types of innovative software application. However, alongside rises the issue of managing the increasing diversity of device characteristics and capabilities. As device fragmentation grows, application developers are facing the need to cover a wider variety of target devices and usage scenarios. In result, maintaining a viable balance between development costs and market coverage has turned out to be an important challenge when developing applications for a ubiquitous ecosystem. In this article, we present the webinos platform, a distributed Web runtime platform that leverages the Web for supporting self-adaptive cross-device applications. In order to enable the development of such immersive ubiquitous applications, we introduce and evaluate the concept of a context-aware federated overlay architecture

Developing Adaptive and Personalized Mobile Applications: A Framework and Design Issues

The rapid growth of mobile technology has expedited ubiquitous information access via handheld devices. However, the fundamental natures of mobile information systems are different from those of desktop applications in terms of purpose of use, device features, communication networks, and working environments. This poses various challenges to mobile information systems on how to deliver and present multimedia content in an effective and adaptive manner. One of the major challenges is to deliver personalized information to the right person in a preferred format based on the changing environment. This paper proposes an innovative framework for developing mobile applications that deliver personalized, context-aware, and adaptive content to mobile users. The framework consists of four major components: information selection, content analysis, media transcoding, and customized presentation. It can be applied to a variety of mobile applications such as mobile web, news alert services, and mobile commerce

Managing contextual information in semantically-driven temporal information systems

Context-aware (CA) systems have demonstrated the provision of a robust solution for personalized information delivery in the current content-rich and dynamic information age we live in. They allow software agents to autonomously interact with users by modeling the user’s environment (e.g. profile, location, relevant public information etc.) as dynamically-evolving and interoperable contexts. There is a flurry of research activities in a wide spectrum at context-aware research areas such as managing the user’s profile, context acquisition from external environments, context storage, context representation and interpretation, context service delivery and matching of context attributes to users‘ queries etc. We propose SDCAS, a Semantic-Driven Context Aware System that facilitates public services recommendation to users at temporal location. This paper focuses on information management and service recommendation using semantic technologies, taking into account the challenges of relationship complexity in temporal and contextual information

A linked data compliant framework for dynamic and web-scale consumption of web services

The While Semantic Web Services (SWS) research aims at automating Web service tasks such as discovery, orchestration and execution, its take-up is very limited so far. This is due to several reasons, such as inherent complexity of existing SWS frameworks and the considerable costs involved in creating correct SWS descriptions. In addition, while semantics are in use to enable tasks such as discovery, interaction between service consumers, providers and brokering environments is still not supported by semantic message descriptions. On the other hand, the Linked Data approach has produced a set of established principles for sharing and describing data, such as RDF as representation language and the integral use of dereferencable URIs. In this paper we propose to apply those principles to expose Web services and Web APIs and introduce a framework in which service registries as well as services contribute to the automation of service discovery, and hence, workload is distributed more efficiently. This is achieved by developing a Linked Data compliant Web services framework with that communicate with semi-centralised registries but compute their suitability for a given request themselves. All communications among different framework components are using RDF-based message protocols including service input and output. This framework aims at optimizing load balance and performance by dynamically assembling services at run time in a massively distributed Web environment

Model Based Development of Quality-Aware Software Services

Modelling languages and development frameworks give support for functional and structural description of software architectures. But quality-aware applications require languages which allow expressing QoS as a first-class concept during architecture design and service composition, and to extend existing tools and infrastructures adding support for modelling, evaluating, managing and monitoring QoS aspects. In addition to its functional behaviour and internal structure, the developer of each service must consider the fulfilment of its quality requirements. If the service is flexible, the output quality depends both on input quality and available resources (e.g., amounts of CPU execution time and memory). From the software engineering point of view, modelling of quality-aware requirements and architectures require modelling support for the description of quality concepts, support for the analysis of quality properties (e.g. model checking and consistencies of quality constraints, assembly of quality), tool support for the transition from quality requirements to quality-aware architectures, and from quality-aware architecture to service run-time infrastructures. Quality management in run-time service infrastructures must give support for handling quality concepts dynamically. QoS-aware modeling frameworks and QoS-aware runtime management infrastructures require a common evolution to get their integration