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- R =
{parentClassOf, includes - in - function, related - to - in - function}
is a
set of relations which are used to construct the conceptual view.
According to the above definitions, the algorithm for extracting a conceptual
view from an OWL-DL based Data-Brain is shown in Algorithm 1. Its input
parameters are the view core Core and the function dimension FD . Its output is
the Core centric conceptual view CV . The function TDR showninAlgorithm2
is used to get the traversal directive result. Its input parameters are C st , R , n ,
and O , which are corresponding to the starter concept, the name of relation,
the depth of the traversal, and the source ontology of the definition of traversal
directive result, respectively.
Using the above algorithms, we can choose different cognitive function con-
cepts as view cores to construct various conceptual views based on various view-
points of BI investigation.
5
Conclusions
The Data-Brain modeling is a core issue of BI study. Aiming at the require-
ments of systematic data management, this paper proposes a new conceptual
data model of brain data, called Data-Brain. By the BI methodology based ap-
proach of Data-Brain modeling, a multi-dimension, formal Data-Brain can be
constructed. It will be the core component of BI portal and play an important
role in systematic BI study by providing the following functions:
- A formal conceptual model of human brain data, which explicitly describes
the relationships among multiple human brain data, with respect to all major
aspects and capabilities of HIPS, for supporting systematic human brain data
storage, integration and sharing;
- A knowledge-base of systematic BI study, which stores brain data related
multi-aspect domain knowledge to both support various knowledge driven
data applications and provide valuable knowledge sources for solving spe-
cial domain problems, such as the diagnosis and treatment for MCI (mild
cognitive impairment) patients;
- A global view and knowledge framework for constructing a BI portal, on
which various methods for data processing, mining, reasoning, and simula-
tion are deployed as agents for implementing the Data-Brain driven multi-
aspect data analysis.
Acknowledgments
The work is partially supported by National Natural Science Foundation of
China (No. 60673015), the grant-in-aid for scientific research (No. 18300053)
from the Japanese Society for the Promotion of Science, and Support Center for
Advanced Telecommunications Technology Research, Foundation.
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