CARSA : A context-aware reasoning-based service agent model for AI planning of web service compostion

In order to achieve automatic and more intelligent service composition, dynamic description logic (DDL) is proposed and utilized as one emerging logic-level solution. However, reasoning optimization and utilization in such DDL-related solutions is still an open problem. In this paper, we propose the context-aware reasoning-based service agent model (CARSA) which exploits the relationships among different service consumers and providers, together with the corresponding optimization approach to strengthen the effectiveness of Web service composition. Through the model, two reasoning optimization methods are proposed based on the substitute relationship and the dependency relationship, respectively, so irrelevant actions can be filtered out of the reasoning space before the DDL reasoning process is carried out. The case study and experimental analysis demonstrates the capability of the proposed approach.

Context aware privacy in visual surveillance

In this paper we present preliminary work implementing dynamic privacy in public surveillance. The aim is to maximise the privacy of those under surveillance, while giving an observer access to sufficient information to perform their duties. As these aspects are in conflict, a dynamic approach to privacy is required to balance the system's purpose with the system's privacy. Dynamic privacy is achieved by accounting for the situation, or context, within the environment. The context is determined by a number of visual features that are combined and then used to determine an appropriate level of privacy.

Between context-aware media capture and multimedia content analysis - where do we find the promised land?

Various issues related to the multimedia information retrieval and media access are discussed. The feasible solutions for automatic signal-based analysis of media content are analyzed. The extent of user involvement in the content creation process is emphasized. The applications driving the creation and usage of context and metadata are also elaborated.

Context-aware meta search engine for distributed web service

The ubiquity of the Internet and Web has led to the emergency of several Web search engines with varying capabilities. A weakness of existing search engines is the very extensive amount of hits that they can produce. Moreover, only a small number of web users actually know how to utilize the true power of Web search engines. Therefore, there is a need for searching infrastructure to help ease and guide the searching efforts of web users toward their desired objectives. In this paper, we propose a context-based meta-search engine and discuss its implementation on top of the actual Google.com search engine. The proposed meta-search engine benefits the user the most when the user does not know what exact document he or she is looking for. Comparison of the context-based meta-search engine with both Google and Guided Google shows that the results returned by context-based meta-search engine is much more intuitive and accurate than the results returned by both Google and Guided Google.

A graph-based model for context-aware recommendation using implicit feedback data

Recommender systems have been successfully dealing with the problem of information overload. However, most recommendation methods suit to the scenarios where explicit feedback, e.g. ratings, are available, but might not be suitable for the most common scenarios with only implicit feedback. In addition, most existing methods only focus on user and item dimensions and neglect any additional contextual information, such as time and location. In this paper, we propose a graph-based generic recommendation framework, which constructs a Multi-Layer Context Graph (MLCG) from implicit feedback data, and then performs ranking algorithms in MLCG for context-aware recommendation. Specifically, MLCG incorporates a variety of contextual information into a recommendation process and models the interactions between users and items. Moreover, based on MLCG, two novel ranking methods are developed: Context-aware Personalized Random Walk (CPRW) captures user preferences and current situations, and Semantic Path-based Random Walk (SPRW) incorporates semantics of paths in MLCG into random walk model for recommendation. The experiments on two real-world datasets demonstrate the effectiveness of our approach.

HorusCML: context-aware domain-specific visual languages designer

The objective behind building domain-specific visual languages (DSVLs) is to provide users with the most appropriate concepts and notations that best fit with their domain and experience. However, the existing DSVL designers do not support integrating environment and user context information when modeling, editing or viewing DSVL models at different locations, permissions, devices, etc. In this paper, we introduce HorusCML, a context-aware DSVL designer, which supports DSVL experts in integrating necessary context details within their DSVLs. The resultant DSVLs can reflect different facets, layouts, and behaviours according to context it is used in. We show a case study on developing a context-aware data flow diagram DSVL tool using HorusCML.

Exploring the effectiveness of true abnormal data elimination in context-aware web services recommendation

Recent years have witnessed a growing interest in context-aware recommender system (CARS), which explores the impact of context factors on personalized Web services recommendation. Basically, the general idea of CARS methods is to mine historical service invocation records through the process of context-aware similarity computation. It is observed that traditional similarity mining process would very likely generate relatively big deviations of QoS values, due to the dynamic change of contexts. As a consequence, including a considerable amount of deviated QoS values in the similarity calculation would probably result in a poor accuracy for predicting unknown QoS values. In allusion to this problem, this paper first distinguishes two definitions of Abnormal Data and True Abnormal Data, the latter of which should be eliminated. Second, we propose a novel CASR-TADE method by incorporating the effectiveness of True Abnormal Data Elimination into context-aware Web services recommendation. Finally, the experimental evaluations on a real-world Web services dataset show that the proposed CASR-TADE method significantly outperforms other existing approaches.

Facilitating dynamic web service composition with fine-granularity context management

Context is an important factor for the success of dynamic service composition. Although many contextbased AI or workflow approaches have been proposed to support dynamic service composition, there is still an unaddressed issue of the support of fine-granularity context management. In this paper, we propose a granularity-based context model together with an approach to supporting the intelligent context-aware service composing problem. The corresponding case study is provided to show the validity of our approach.

Context-based e-health system access control mechanism

E-Health systems logically demand a sufficiently fine-grained authorization policy for access control. The access to medical information should not be just role-based but should also include the contextual condition of the role to access data. In this paper, we present a mechanism to extend the standard role-based access control to incorporate contextual information for making access control decisions in e-health application. We present an architecture consisting of authorisation and context infrastructure that work cooperatively to grant access rights based on context-aware authorization policies and context information.

A shadow-like task migration model based on context semantics for mobile and pervasive environments

Pervasive computing is a user-centric mobile computing paradigm, in which tasks should be migrated over different platforms in a shadow-like way when users move around. In this paper, we propose a context-sensitive task migration model that recovers program states and rebinds resources for task migrations based on context semantics through inserting resource description and state description sections in source programs. Based on our model, we design and develop a task migration framework xMozart which extends the Mozart platform in terms of context awareness. Our approach can recover task states and rebind resources in the context-aware way, as well as support multi- modality I/O interactions. The extensive experiments demonstrate that our approach can migrate tasks by resuming them from the last broken points like shadows moving along with the users.

A smart web service based on the context of things

Combining the Semantic Web and the Ubiquitous Web, Web 3.0 is for things. The Semantic Web enables human knowledge to be machine-readable and the Ubiquitous Web allows Web services to serve any thing, forming a bridge between the virtual world and the real world. By using context, Web services can become smarter-that is, aware of the target things' or applications' physical environments, or situations and respond proactively and intelligently. Existing methods for implementing context-aware Web services on Web 2.0 mainly enumerate different implementations corresponding to different attribute values of the context, in order to improve the Quality of Services (QoS). However, things in the physical world are extremely diverse, which poses new problems for Web services: it is difficult to unify the context of things and to implement a flexible smart Web service for things. This article proposes a novel smart Web service based on the context of things, which is implemented using a REpresentational State Transfer for Things (Thing-REST) style, to tackle the two problems. In a smart Web service, the user's description (semantic context) and sensor reports (sensing context) are two channels for acquiring the context of things which are then employed by ontology services to make the context of things machine-readable. With guidance of domain knowledge services, event detection services can analyze things' needs particularly, well through the context of things. We then propose a Thing-REST style to manage the context of things and user context, and to mashup Web services through three structures (i.e., chain, select, and merge) to implement smart Web services. A smart plant watering-service application demonstrates the effectiveness of our method. © 2012 ACM.

Editorial : towards merging grid and pervasive computing

This special issue is in response to the increasing convergence between grids and pervasive computing, while different approaches exist, challenges and opportunities are numerous in this context (Parashar and Pierson, to appear). The research papers selected for this special issue represent recent progresses in the field, including works on mobile ad-hoc grids, service and data discovery, context-aware application building and context accuracy, and communication. All of these papers not only provide novel ideas and state-of-the-art techniques in the field, but also stimulate future research in the Pervasive Grid environment.