NISMOD-DB++ enables storage and access to large and diverse datasets linked to infrastructure analysis and modelling facilitating the development of the next generation of models.
Utilising new database technologies innovative approaches are applied to create a flexible resource enabling the storage and management of large datasets related to national infrastructure within a developed framework (Figure 1) which under previously developed architectures would not have been as effective or feasible. Datasets added to the database are done so using dataset specific methods and tailored database solutions, ensuring data is stored in the most appropriate way making the developed solution more efficient for both storing and reading data. Using existing web technologies access to the storage solution is maintained for users through an web based API (Application Programming Interface)(Figure 2), enabling users to both read and write data to and from the database as required.
Through the coupling of different database solutions new insights can be learned harnessing the analysis powers embedded within the employed technologies. These new capabilities allow for datasets to be linked in different and new methods within the storage environment enabling more cross-data learning, giving the opportunity for new questions to be explored.
So what, most significant results
Underpinning the potential for complex infrastructure models, NISMOD-DB++ provides a resource for large, complex and varied datasets, as well as supporting more complex analysis through the capability to connect multiple datasets of varied form and structure. Accessed through a web facing API, the developed system is both secure yet open and easily accessible for users.
Through the new technologies employed such as graph databases, a more native solution for storing network data, larger and more complex datasets than otherwise feasibly possible can be stored while maintaining rapid access to the data itself. This enables researchers and models to use data at much finer scales then previously possible facilitating the potential for the new insights in infrastructure research.