Coupling models by routing communication through a database

By Erick Solano Mora, Robert Morris, Georgiy Bobashev

As the number of available large and many-faceted computer models continues to increase, simulating complex systems by coupling existing models of smaller subsystems becomes more attractive because of advantages such as leveraging existing programming. Advances in computational technologies also contribute to the increased feasibility of coupled systems. Although coupled systems may be used to study new problems that their constituent models could not address, the coupling process brings its own challenges. The modeler may face the task of coupling models from a heterogeneous environment of development platforms, programming languages, and model assumptions. Moreover, the modeler may wish to allow constituent models to be replaced or upgraded without significant difficulty. We discuss a model coupling approach that attempts to address these issues. In our approach, the models run as separate executable processes and store data in a database for later retrieval by other models. While the approach does not prescribe any particular database design, we do suggest elements that are likely to appear. We describe a proof-of-concept application of the approach and evaluate how well our approach meets its goals.


Solano Mora, E., Morris, R., & Bobashev, G. (2013). Coupling models by routing communication through a database. (RTI Press Publication No. MR-0026-1309). Research Triangle Park, NC: RTI Press.

© 2019 RTI International. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.


Erick Solano Mora

Robert MorrisRobert J. Morris, BS, who has been with RTI International since 2000, is a programmer and statistician. He performs software development and statistical data analysis as well as programming for quality control, data editing, and data management.

Georgiy BobashevGeorgiy V. Bobashev, PhD, is a senior research statistician and an expert in biostatistics methodology and mathematical modeling. His current research interests cover two major areas: substance-use studies and predictive modeling. In the substance-use research area, he focuses on personalized treatments and a systems approach to addictions. In predictive modeling, he focuses on methods development in forecasting health outcomes, predominantly substance use and risky behavior.

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