Title

MODEL MANAGEMENT FOR HYPOTHESIS-DRIVEN SIMULATION EXPERIMENT WORKFLOWS

Abstract

Abstract

With today's breakthroughs in computational science and engineering, research experts can now
simulate a lot of experiments on computers. Experiment specification is aided by frameworks and
support systems for reusability and reproducibility of scientific research, as well as domain-specific
languages, domain models, ontologies, data models, statistical analysis methods, and other types of
tools and assets with related formalisms. Despite this, most frameworks or support tools for experiment
specification ignore hypotheses and lack a procedure based on properly stated hypotheses. The main
issue with a lack of hypotheses in the experimental process is that an experiment's credibility and
repeatability can be harmed by an erroneous or inadequate record. Furthermore, the diversity of
models, metamodels, tools, and data for testing bring the need for Global Model Management (GMM).
In that sense, GMM leverages documenting, sharing, reusability, and replicability of simulation
experiments by employing Model-Driven Engineering methodologies.
This thesis demonstrates how to use GMM to facilitate simulation experimentation with explicit
hypotheses as a scientific workflow and proposes an extension to the Simulation Experiment Description
Markup Language (SED-ML) that involves explicit specification of the hypothesis targeted in the
simulation experiment. A megamodel, or registry for models and metamodels, is created particularly to
serve as a repository for managing the artifacts used in a simulation project. All steps of a simulation
experiment, including specification, input data production, experiment execution, and output data
analysis, are effectively addressed by the megamodel. Then, using case studies, the applicability of GMM
to simulation experiments is demonstrated. GMM, in our view, provides a solid framework for managing both experiment assets and experiment processes.

Supervisor(s)

Supervisor(s)

SEMA CAM

Date and Location

Date and Location

2022-09-05 10:00:00

Category

Category

PhD_Thesis