MultimediaN: Ambient Databases

The aim of the project is to deliver an ambient data management solution towards ambient intelligence. A P2P based multimedia database infrastructure will be built up to support personalized and context-aware database access in a highly distributed, mobile, heterogeneous and ad-hoc organized environment. With such a framework, future ambient intelligent applications can be easily constructed, similar to the way relational database technology has eased the development of traditional business applications. One particular issue to be addressed in this project is "Semantic Frontend", which aims at advancing the state-of-the-art in (semi-)automatic semantic schema integration and context-based querying using semantic web and ontology technologies. Under this project, two sub-projects are defined.


Rule-based schema integration: This subproject mainly focuses on the design and realization of a schema mapping language and corresponding system for XML. A starting point for the subproject is the source of possibly suitable techniques from the area of view definition and materialization. It should be investigated if and how these techniques can be adapted to the XML-context. In a later phase, we investigate if and how other technologies, like semantic web and ontology, can be used for generating appropriate mapping rules for XML. As a starting point, we will investigate how the various forms of context (e.g., location and time) can guide and simplify the task of generating schema mapping rules.


Context-aware querying: In this subproject, we investigate how querying services can be made more intelligent allowing one to pose questions in terms of users' contexts instead of only the exact underlying schema. Two kinds of context (i.e., external context and internal context) will be investigated for context-aware querying. As a starting point, we observe that context can be seen as an indicator for relevance of certain data. Therefore, one of the objectives is to find out if and how IR-like relevance-querying techniques can be adapted or applied for context-aware querying. At a high level, the context-aware query language, together with its query processing and optimization strategies have the great potential of being explored for context-aware querying as well.