Distributed mashups: a collaborative approach to data integration

This paper aims to present a collaborative mashup platform for dynamic integration of heterogeneous data sources. The platform encourages sharing and connects data publishers, integrators, developers and end users.

This approach is based on a visual programming paradigm and follows three fundamental principles: openness, connectedness and reusability. The platform is based on semantic Web technologies and the concept of linked widgets, i.e. semantic modules that allow users to access, integrate and visualize data in a creative and collaborative manner.

The platform can effectively tackle data integration challenges by allowing users to explore relevant data sources for different contexts, tackling the data heterogeneity problem and facilitating automatic data integration, easing data integration via simple operations and fostering reusability of data processing tasks.

This research has focused exclusively on conceptual and technical aspects so far; a comprehensive user study, extensive performance and scalability testing is left for future work.

A key contribution of this paper is the concept of distributed mashups. These ad hoc data integration applications allow users to perform data processing tasks in a collaborative and distributed manner simultaneously on multiple devices. This approach requires no server infrastructure to upload data, but rather allows each user to keep control over their data and expose only relevant subsets. Distributed mashups can run persistently in the background and are hence ideal for real-time data monitoring or data streaming use cases. Furthermore, we introduce automatic mashup composition as an innovative approach based on an explicit semantic widget model.