Robust and scalable user context management is the key enabler for the emerging context- and situation-aware applications, and ontology-based approaches have shown their usefulness for capturing especially context information on a high level of abstraction. But so far the problem has not been approached as a data management problem, which is key to scalability and robustness. The specific challenges lie in the imperfection of high-level context information, its time-dependence and the variability in the dynamics between its different elements.

Abstract. High quality context information plays a vital role in adapting a system to the rapidly changing situations. However, the diversity of the sources of context information and the characteristics of the computing devices strongly impact the quality of context information in pervasive computing environments. Quality of Context parameters can be used to characterize the quality of context information from different aspects. In this paper, we quantify the Quality of Context parameters to present them in a suitable form for use with the applications in pervasive environments. We also present a mechanism to tailor the Quality of Context parameters according to the needs of an application and then evaluate these parameters. Enrichment of context information with Quality of Context parameters enhances the capabilities of context-aware systems to effectively use the context information to adapt to the emerging situations in pervasive computing environments. 1

Sensing context information and making it available to the people, involved in coordinating a collaborative task, is a preliminary phase in making a system adaptable to the prevailing situation in pervasive environments. However, the diversity of the sources of context information, the characteristics of pervasive environments, and the nature of collaborative tasks pose a stern challenge to the efficient management of context information by sensing a lot of redundant and conflicting information. Quality of Context parameters can be used to resolve the conflicts in context information. In this paper, we present a context aggregation system that detects and removes the duplicates and conflicts from context information by using the policies based on Quality of Context parameters. This system effectively aggregates the continuously evolving context information and efficiently uses the scare resources in pervasive environments. 1.

User experience while searching for web pages on the move can be far from satisfactory due to the inherent limitations of the input modes available in mobile devices. On the other hand, end-users can benefit from the availability of contextaware information anywhere, anytime. To overcome the usability problem and exploit context information at the same time, we propose a thesaurus-based semantic context-aware autocompletion mechanism. Our system can help the user in completing the desired query terms avoiding manual typing. In addition we are capable of filtering out non-relevant query terms for the Context in which the search process is conducted. Our context-aware proposal is based on a model which represents formally all the information about the user circumstances, the access mechanism (device and web browser) and the surrounding environment. Our evaluation reveals that users can find new relevant context-aware results with less effort. 1.