Daqing Zhang

Bio: Daqing Zhang is an academic researcher from Peking University. The author has contributed to research in topic(s): Context (language use) & Mobile computing. The author has an hindex of 67, co-authored 331 publication(s) receiving 16675 citation(s). Previous affiliations of Daqing Zhang include Institut Mines-Télécom & Institute for Infocomm Research Singapore.

Ontology based context modeling and reasoning using OWL

Abstract: Here we propose an OWL encoded context ontology (CONON) for modeling context in pervasive computing environments, and for supporting logic-based context reasoning. CONON provides an upper context ontology that captures general concepts about basic context, and also provides extensibility for adding domain-specific ontology in a hierarchical manner. Based on this context ontology, we have studied the use of logic reasoning to check the consistency of context information, and to reason over low-level, explicit context to derive high-level, implicit context. By giving a performance study for our prototype, we quantitatively evaluate the feasibility of logic based context reasoning for nontime-critical applications in pervasive computing environments, where we always have to deal carefully with the limitation of computational resources.

A service-oriented middleware for building context-aware services

Abstract: The advancement of wireless networks and mobile computing necessitates more advanced applications and services to be built with context-awareness enabled and adaptability to their changing contexts. Today, building context-aware services is a complex task due to the lack of an adequate infrastructure support in pervasive computing environments. In this article, we propose a Service-Oriented Context-Aware Middleware (SOCAM) architecture for the building and rapid prototyping of context-aware services. It provides efficient support for acquiring, discovering, interpreting and accessing various contexts to build context-aware services. We also propose a formal context model based on ontology using Web Ontology Language to address issues including semantic representation, context reasoning, context classification and dependency. We describe our context model and the middleware architecture, and present a performance study for our prototype in a smart home environment.

An Ontology-based Context Model in Intelligent Environments

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.

A middleware for building context-aware mobile services

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.

Modeling User Activity Preference by Leveraging User Spatial Temporal Characteristics in LBSNs

Abstract: With the recent surge of location based social networks (LBSNs), activity data of millions of users has become attainable. This data contains not only spatial and temporal stamps of user activity, but also its semantic information. LBSNs can help to understand mobile users’ spatial temporal activity preference (STAP), which can enable a wide range of ubiquitous applications, such as personalized context-aware location recommendation and group-oriented advertisement. However, modeling such user-specific STAP needs to tackle high-dimensional data, i.e., user-location-time-activity quadruples, which is complicated and usually suffers from a data sparsity problem. In order to address this problem, we propose a STAP model. It first models the spatial and temporal activity preference separately, and then uses a principle way to combine them for preference inference. In order to characterize the impact of spatial features on user activity preference, we propose the notion of personal functional region and related parameters to model and infer user spatial activity preference. In order to model the user temporal activity preference with sparse user activity data in LBSNs, we propose to exploit the temporal activity similarity among different users and apply nonnegative tensor factorization to collaboratively infer temporal activity preference. Finally, we put forward a context-aware fusion framework to combine the spatial and temporal activity preference models for preference inference. We evaluate our proposed approach on three real-world datasets collected from New York and Tokyo, and show that our STAP model consistently outperforms the baseline approaches in various settings.

Context Aware Computing for The Internet of Things: A Survey

Abstract: As we are moving towards the Internet of Things (IoT), the number of sensors deployed around the world is growing at a rapid pace. Market research has shown a significant growth of sensor deployments over the past decade and has predicted a significant increment of the growth rate in the future. These sensors continuously generate enormous amounts of data. However, in order to add value to raw sensor data we need to understand it. Collection, modelling, reasoning, and distribution of context in relation to sensor data plays critical role in this challenge. Context-aware computing has proven to be successful in understanding sensor data. In this paper, we survey context awareness from an IoT perspective. We present the necessary background by introducing the IoT paradigm and context-aware fundamentals at the beginning. Then we provide an in-depth analysis of context life cycle. We evaluate a subset of projects (50) which represent the majority of research and commercial solutions proposed in the field of context-aware computing conducted over the last decade (2001-2011) based on our own taxonomy. Finally, based on our evaluation, we highlight the lessons to be learnt from the past and some possible directions for future research. The survey addresses a broad range of techniques, methods, models, functionalities, systems, applications, and middleware solutions related to context awareness and IoT. Our goal is not only to analyse, compare and consolidate past research work but also to appreciate their findings and discuss their applicability towards the IoT.

A survey on context-aware systems

Abstract: Context-aware systems offer entirely new opportunities for application developers and for end users by gathering context data and adapting systems behaviour accordingly. Especially in combination with mobile devices, these mechanisms are of high value and are used to increase usability tremendously. In this paper, we present common architecture principles of context-aware systems and derive a layered conceptual design framework to explain the different elements common to most context-aware architectures. Based on these design principles, we introduce various existing context-aware systems focusing on context-aware middleware and frameworks, which ease the development of context-aware applications. We discuss various approaches and analyse important aspects in context-aware computing on the basis of the presented systems.