A meta-composite software development approach for translational research
Department of Quantitative Health Sciences
Bioinformatics | Health Information Technology | Software Engineering | Translational Medical Research
Translational researchers conduct research in a highly data-intensive and continuously changing environment and need to use multiple, disparate tools to achieve their goals. These researchers would greatly benefit from meta-composite software development or the ability to continuously compose and recompose tools together in response to their ever-changing needs. However, the available tools are largely disconnected, and current software approaches are inefficient and ineffective in their support for meta-composite software development. Building on the composite services development approach, the de facto standard for developing integrated software systems, we propose a concept-map and agent-based meta-composite software development approach. A crucial step in composite services development is the modeling of users' needs as processes, which can then be specified in an executable format for system composition. We have two key innovations.
First, our approach allows researchers (who understand their needs best) instead of technicians to take a leadership role in the development of process models, reducing inefficiencies and errors. A second innovation is that our approach also allows for modeling of complex user interactions as part of the process, overcoming the technical limitations of current tools. We demonstrate the feasibility of our approach using a real-world translational research use case. We also present results of usability studies evaluating our approach for future refinements.
DOI of Published Version
Sadasivam RS, Tanik MM. A meta-composite software development approach for translational research. J Med Syst. 2013 Jun;37(3):9935. doi:10.1007/s10916-013-9935-6. Link to article on publisher's site
Journal of medical systems
Sadasivam RS, Tanik MM. (2013). A meta-composite software development approach for translational research. Population and Quantitative Health Sciences Publications. https://doi.org/10.1007/s10916-013-9935-6. Retrieved from https://escholarship.umassmed.edu/qhs_pp/1075