Author
Xiao Hang Wang
Bio: Xiao Hang Wang is an academic researcher from Agency for Science, Technology and Research. The author has contributed to research in topics: Middleware & Ontology (information science). The author has an hindex of 1, co-authored 1 publications receiving 432 citations.
Papers
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TL;DR: A formal context model based on ontology using OWL is proposed to address issues including semantic context representation, context reasoning and knowledge sharing, context classification, context dependency and quality of context.
Abstract: Computing becomes increasingly mobile and pervasive today; these changes imply that applications and services must be aware of and adapt to their changing contexts in highly dynamic environments. Today, building context-aware systems is a complex task due to lack of an appropriate infrastructure support in intelligent environments. A context-aware infrastructure requires an appropriate context model to represent, manipulate and access context information. In this paper, we propose a formal context model based on ontology using OWL to address issues including semantic context representation, context reasoning and knowledge sharing, context classification, context dependency and quality of context. The main benefit of this model is the ability to reason about various contexts. Based on our context model, we also present a Service-Oriented Context-Aware Middleware (SOCAM) architecture for building of context-aware services.
438 citations
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TL;DR: The requirements that context modelling and reasoning techniques should meet are discussed, including the modelling of a variety ofcontext information types and their relationships, of situations as abstractions of context information facts, of histories of contextInformation, and of uncertainty of context Information.
1,201 citations
TL;DR: A Service-Oriented Context-Aware Middleware architecture for the building and rapid prototyping of context-aware services and a formal context model based on ontology using Web Ontology Language to address issues including semantic representation, context reasoning, context classification and dependency are proposed.
954 citations
TL;DR: A comprehensive analysis of the nature and characteristics of situations is provided, the complexities of situation identification are discussed, and the techniques that are most popularly used in modelling and inferring situations from sensor data are reviewed.
450 citations
17 May 2004
TL;DR: This work presents a service-oriented context-aware middleware (SOCAM) architecture for the building and rapid prototyping of context- aware mobile services, and proposes an ontology-based approach to model various contexts.
Abstract: Computing becomes increasingly mobile and pervasive today; these changes imply that applications and services must be aware and adapt to highly dynamic environments. Today, building context-aware mobile services is a complex and time-consuming task. We present a service-oriented context-aware middleware (SOCAM) architecture for the building and rapid prototyping of context-aware mobile services. We propose an ontology-based approach to model various contexts. Our context model supports semantic representation, context reasoning and context knowledge sharing. We take a service-oriented approach to build our middleware which supports tasks including acquiring, discovering, interpreting, accessing various contexts and interoperability between different context-aware systems.
416 citations
08 Nov 2004
TL;DR: This paper proposes an adaptable and extensible context ontology for creating context-aware computing infrastructures, ranging from small embedded devices to high-end service platforms.
Abstract: To realise an Ambient Intelligence environment, it is paramount that applications can dispose of information about the context in which they operate, preferably in a very general manner. For this purpose various types of information should be assembled to form a representation of the context of the device on which aforementioned applications run. To allow interoperability in an Ambient Intelligence environment, it is necessary that the context terminology is commonly understood by all participating devices. In this paper we propose an adaptable and extensible context ontology for creating context-aware computing infrastructures, ranging from small embedded devices to high-end service platforms. The ontology has been designed to solve several key challenges in Ambient Intelligence, such as application adaptation, automatic code generation and code mobility, and generation of device specific user interfaces.
366 citations