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Fig. 1.6 Visual analytics for
knowledge discovery
appropriate SV representations are required to be automatically involved, when
such task is executed as part of the multidisciplinary investigation problem. The
goal is to find an appropriate way to integrate the relevant domain knowledge, in
order to become part of the envisaged integrated multidisciplinary knowledge
base. It seems that an appropriate software solution can be found in Visual Ana-
lytics (VA) methodology [ 23 , 24 ], which combines SV with the information data
mining, emerging as an important research line to follow, see Fig. 1.6 .VAis
enlarging SV, and can be considered as an extended approach in the developments
of multidisciplinary visualization features by integrating data analysis know-how.
Especially, the VA research in the human-computer interaction is focusing on:
collaboration, interaction, presentation, and dissemination, based on utilizing
knowledge representation for knowledge discovery.
The software development process of such complex multidisciplinary software
requires the knowledge engineering approach, and more specifically the ontology
modeling [ 25 ], where each software component is expected to have its own
ontology model and the respective knowledge base associated to it. For example,
there will be the possibility to query the data model structure and its related
functionality found in the applied software components, but in a machine pro-
grammable way, thus avoiding end-user input. The expected benefit of such
approach is that the software integration will become more automatic and less
error prone, when achieved by interrogating ontology and the related knowledge
by the program-to-program, or machine-to-machine based way. Such integration
process will take control over the data and processes, through the well-defined
functionality, which will be enriched with the relevant semantics. These semantics
components will enable the applications and users to explore/reuse/enlarge the
domain engineering knowledge—present within applications (software-coded) and
will continuously evolve in such future software solutions.
The basic design principle for the ontology modeling is to support the features
that are necessary for a specific application domain (user-engineer point of view).
The ontology models represent the domain concepts and their relationships, thus
they define the domain language (semantics) meaningful to humans and usable by
machines, in order to improve:
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