Included are the hypothesis-driven principle, deferred decision/multiple-hypotheses principle, information integration principle, alternative solution principle, and perturbation principle.
Both of these elements are consistent with Australian government principles for data integration [ 21].
At the same time as the PHRN and CDL were developing, a number of Commonwealth government agencies came together to establish a set of guiding principles for data integration involving Commonwealth data [ 21].
As we have shown, the semantic web has the potential to assist in many of the difficulties posed by the integration of data from disparate sources, as four of its underlying principles accelerate data integration and its exploration: Represent data and knowledge with technologies that serve as a standard across the entire community.
It follows four principles: data cleaning, data integration, data transformation, and data reduction.
Besides the microarray data integration, we also have applied the principle to the clustering problem and have introduced a novel clustering framework, SignatureClust [18].
In principle, technical and semantic data integration can be performed during data import.
The unified biological ontology provides integration principles and reasoning rules to data integration module and distributed reasoning module, respectively.
One possible explanation for this apparent contradiction would be that the first two principle components and the clustering trees obtained after data integration reflect biologically irrelevant technical variance not related to data source.
Besides the conventional distributed schemes, it is very natural to try and apply well-known turbo principle [1], which has been employed in turbo equalization [3, 4], Bayesian data integration [7, 8], and turbo BS cooperations [5 7, 9].
