Abstract
Quite often, in order to investigate and understand how a real life system works, we construct a model of the system, at an abstract level. A simulation model consists of input variables, system entities, and functional relationships (Maria, 1997), as well as sets of rules, which define the state transitions of among system entities. Simulation is a mechanism to evaluate, over time, the behavior of a working (or, under construction) system, understand it and explain its performance. Given a set of inputs, the model is run and the simulated behavior and performance are observed.
A simulator for a specific experimental procedure and specific instruments cannot, in principle, be used without changes in a similar experimental environment, since the availability of the instruments also affects the experimental procedure, as different instruments might require certain changes in the experimental steps. The proposed research will ultimately aim to produce a semi-automatic mapping (or transformation) from a defined set of experimental instruments, procedures/experiments and learning outcomes, to a target set of available instruments, prescribed procedures and learning outcomes, which, in principle, may be similar (but not identical) to the initially defined ones.
This transformation/mapping will likely be based on description languages (such as of the HDL-type) and visual modeling environments and tools (i.e. UML, Rational rose), which use state diagrams to describe abstractly the procedures-steps of an experimental process in an algorithmic way. Given the source description of the experimental procedure (experimental instruments, procedures/experiments, learning outcomes) and the respective target description, the proposed transformation algorithm will make the mapping.
Advisory Committee
Supervisor: Dimitrios Kalles, Associate Professor, HOU
Vasilis Verykios, Professor, HOU
George Manis, Assistant Professor, University of Ioannina