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cannot get enough attention. Because of the neglect of conceptual data model-
ing, it is dicult to support systematic BI study based on these brain databases
since the relationships among data cannot be explicitly represented and applied.
For example, systematic data analysis needs to adopt agent-enriched human
brain data mining for multi-aspect analysis and simulation on multiple human
brain data sources. This needs not only systematic data storage based on the
relationships among data, but also a formal conceptual model which explicitly
describes the relationships among data to guide agent computing. Hence, sys-
tematic BI methodology needs the study of conceptual modeling of brain data,
i.e., Data-Brain modeling.
The complexity of human brain leads that BI study needs global cooperation.
Thus, more and more researchers focus their efforts on connecting decentralized
brain databases by network or grid infrastructure to construct various resource
networks/grids for supporting global cooperation. The international neuroin-
formatics network is just such a resource network, which contains many brain
database nodes, such as Brain Bank [17]. At the network/grid level, conceptual
data models have the wider scope of applications, including not only off-time
applications, such as supporting the design of database schemas, but also on-
time applications, such as providing formal knowledge sources. Obviously, the
traditional graphical modeling languages, such as the Entity-Relationship (ER)
model [1], cannot meet all the requirements of new applications.
At present, ontologies are widely applied on network/grid based systems [6].
Both ontologies and data models are partial accounts of conceptualizations [9],
and the common features between them have gotten attention [5]. Though some
researchers focus on differentiating ontologies from conceptual data models [4],
the various applications of ontologies, especially the applications in resource
networks/grids, still make the ontology be a new effective approach for formally
modeling data related domain knowledge at the conceptual level. Hence, on-
tology can be regarded as a new approach of conceptual data modeling at the
network/grid level.
3
Data-Brain
3.1
What Is a Data-Brain?
The Data-Brain is a conceptual brain data model, which represents functional re-
lationships among multiple human brain data sources, with respect to all major
aspects and capabilities of HIPS, for systematic investigation and understand-
ing of human intelligence. On one hand, developing such a Data-Brain is a core
research issue of BI. Systematic BI study needs a Data-Brain to describe multi-
aspect data related knowledge for supporting systematic data storage, sharing,
and utilization. Based on this way, it provides a long-term, holistic vision to
uncover the principles and mechanisms of underlying HIPS. On the other hand,
BI methodology supports such a Data-Brain construction. As a network/grid
level of conceptual data model, the Data-Brain can adopt an ontological mod-
eling approach based on BI methodology. In other words, the Data-Brain goes
