Annual Report 2010 - Fachgruppe Informatik an der RWTH Aachen ...

able to easily identify areas where certain combinations of values are present in the data and
to cross-link this data to medical statistics on the nasal respiration. Ultimately, this crossreferencing
should lead to the establishment of the aforementioned objective criteria, which
facilitate an evaluation of any given nasal geometry.
Explorative Analysis of Multiple, Heterogeneous Simulation Data
P. Cerfontaine, T. Beer, T. Kuhlen, C. Bischof
In the “Cluster of Excellence: Integrative Production Technology for High-Wage Countries”
we are developing a flexible framework to enable linked, distributed simulations of entire
manufacturing processes. Our goal is to provide the technological means and methods
required to:
1. Facilitate data exchange and easy transfer of results from one simulation to another
through a common language that is powerful enough to convey all the necessary
information.
2. Take advantage of distributed soft- and hardware resources with an appropriate
middleware that has a sufficient layer of abstraction to encapsulate system specific
details behind a standardized interface.
3. Establish a unified mechanism to describe single simulation jobs as well as complex
workflows consisting of several simulations working hand in hand to model a certain
production process.
4. Further insight and understanding of the process as a whole using leading edge
Virtual Reality technology to visualize and adequately post process the computed
results of all simulations involved in the same process.
To overcome syntactic and semantic differences between data formats employed in the
simulation programs, we introduce a common language capable of handling all sorts of data
types and field attributes that occur. Aside from asserting the reusability of tools operating on
this common data language, this strategy will supposedly minimize the workload overhead
required to integrate new simulations. Our approach reflects industrial reality as closely as
possible. For an easy integration we take advantage of already available infrastructure,
processing power and tools to keep additional investments in terms of hardware and licenses
as low as possible. Through establishing methods to formalize simulation runs into a common
framework we gain sufficient abstraction from system and software specificities to enable
dynamic workflow construction encompassing several simulations. In contrast to traditional
scientific visualization, which focuses on a single dataset, it is of the outmost importance to
be able to find and identify interdependencies and causalities between several simulation
datasets. This forces us to integrate all simulation results from a processing chain into a single
visualization context running at interactive frame rates. Being able to understand the whole
process for the various simulation experts makes it mandatory for our framework to preserve
and reconstruct the time and space coherency between datasets thus reflecting the real process
as closely as possible.
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