Schema merging is the process of consolidating multiple schemas into a unified view. The task becomes particularly challenging when the schemas are highly heterogeneous and autono...
Xiang Li 0002, Christoph Quix, David Kensche, Sand...
Today’s data integration systems must be flexible enough to support the typical iterative and incremental process of integration, and may need to scale to hundreds of data sour...
There has been considerable past work studying data integration and uncertain data in isolation. We develop the foundations for local-as-view (LAV) data integration when the sourc...
Parag Agrawal, Anish Das Sarma, Jeffrey D. Ullman,...
Abstract: Large biomedical projects often include workflows running across institutional borders. In these workflows, data describing biomedical entities, such as patients, bio-m...
We present a modular breakdown of data integration tasks and the results of a survey on the distribution of effort among those tasks. The modularization aids in project planning a...
Leonard J. Seligman, Arnon Rosenthal, Paul E. Lehn...
The process of building a new database relevant to some field of study in biomedicine involves transforming, integrating, and cleansing multiple data sources, as well as adding ne...
Abstract-Wikipedia is an example of the collaborative, semi-structured data sets emerging on the Web. These data sets have large, nonuniform schema that require costly data integra...
Bryan Chan, Leslie Wu, Justin Talbot, Mike Cammara...
Abstract. We address the problem of answering queries using expressive symmetric inter-schema constraints which allow to establish mappings between several heterogeneous informatio...
Genomic medicine aims to revolutionize health care by applying our growing understanding of the molecular basis of disease. Research in this arena is data intensive, which means d...
While providing a uniform syntax and a semistructured data model, XML does not express semantics but only structure such as nesting information. In this paper, we consider the prob...