Information Technology Reference
In-Depth Information
and memory are some component functions to implement human reasoning, as
well as (information) granulation, autonomy, stability and uncertainty are some
interesting characteristics, which need to be investigated with respect to human
reasoning, as illustrated in the middle circle of this figure. Furthermore, decision-
making, problem-solving, planning, computation, language, learning, discovery
and creativity are the major human thinking related functions, which will be
studied systematically, as illustrated outside the middle circle of this figure.
Problem-Solving
Decision-Making
Planning
memory
emotion
granularity
deduction
Reasoning
uncertainty
search
(Commonsense)
Learning
induction abduction
autonomy
Computation
stability
attention
Discovery
Language
Creativity
Fig. 2.
The “Reasoning” centric conceptual view
Secondly, the conceptual view of the Data-Brain can be transformed into
its own structural view with four dimensions, namely function dimension, data
dimension, experiment dimension, and analysis dimension, respectively. Figure 1
illustrates such a transformation. Here we give more descriptions on the four
dimensions as follows:
-
The
function dimension
is a conceptual model of domain knowledge aim-
ing at the systematic investigation in BI methodology. It describes the in-
formation processing courses of human thinking centric cognitive functions
and functional relationships among them at the conceptual level. As stated
above, the thinking centric cognitive functions are complex and closely re-
lated to each other, so Data-Brain needs to include a function dimension.
The function dimension provides a holistic, conceptual functional model of
human brain for systematic BI study. It also provides a machine-readable
knowledge base for constructing various conceptual views.
-
The
data dimension
is a conceptual model of domain knowledge aiming
at systematic brain data storage. It describes multiple views, schemes, and
organizations of human brain data with multiple data sources, multiple data
forms, multiple levels of data granularity at the conceptual level. Conceptual
modeling heterogeneous brain data using the data dimension is the key to
realize systematic data storage which is the base of the systematic data man-
agement in BI methodology. By relations with the function dimension, the
data dimension provides a conceptual data model which represents functional
relationships among multiple human brain data sources obtained from vari-
ous cognitive experiments with respect to all major aspects and capabilities
of HIPS. This kind of data representation is with multi-level by modeling,
