Model management for hypothesis-driven simulation experiment workflows

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2022-9-05

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Çam, Sema

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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.

Subject Keywords

Design of experiments, Global model management, Model driven engineering, Simulation experiment description markup language, Signal temporal logic

URI

https://hdl.handle.net/11511/98751

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Graduate School of Natural and Applied Sciences, Thesis

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Citation Formats

S. Çam, “Model management for hypothesis-driven simulation experiment workflows,” Ph.D. - Doctoral Program, Middle East Technical University, 2022.