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The BIODB system enables multi-model data federation, i.e., it federate data that can be in different data models including, relational, XML and RDF, sequence data and so on.
While, first and foremost in the Grid computing domain, there are already standards and solutions in place addressing related problems, our solution differs from those approaches by allowing to federate data storage systems that are not aware of being federated.
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In this paper we study the problem of provisioning/embedding for evolving/dynamic VDC request across federated data centers, such that the total operation cost is minimized.
Building federated data sharing architectures requires supporting a range of data owners, effective and validated semantic alignment between data resources, and consistent focus on end-users.
The methods described in this article characterize the feasibility of combinations of logical and probabilistic inference with federated data integration for both point and regional SNP annotation and analysis.
A more common source for contradictions is federated data from multiple sources such as data exchange [7], data fusion [8, 9] and data warehousing [10, 11].
Consequently, the ICGC has adopted federated data architecture to address their data management needs.
We found significant variation in the infrastructure existing at these organizations that could support federated data sharing.
To broaden the visibility and interoperability, we have also released the project results to the Neuroscience Information Framework (NIF) federated data repository (https://www.neuinfo.org/mynif/databaseList.php).
Since we follow a federated data integration strategy by using Web Services, the autonomy and up-to-dateness of all systems is granted at any time.
These include Chado [ 2], a database schema for genomic data, several genome synteny browsers [ 3 5], the Galaxy workflow engine [ 6], the Apollo genome editor [ 7], the MAKER genome annotation pipeline [ 8] and the BioMart federated data mining engine [ 9].
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