Robustness of Spatial Databases: Using Network Analysis on GIS Data Models

Abstract: Demands on the quality and reliability of Volunteered Geographic Information have increased because of its rising popularity. Due to the less controlled data entry, there is a risk that people provide false or inaccurate information to the database. One factor that affects the effect of such updates is the network structure of the database schema, which might reveal the database’s robustness against different kinds of false updates. Therefore, network analyses are needed. The aim is to analyse GIS data models, stored in UML class diagrams, for scale-free and small-world properties. Moreover, a robustness analysis is to be carried out on selected data models in order to find out their error and attack tolerance against, for example, false updates. Three graphs were specified from the UML class diagrams: (1) class graphs: classes as nodes and their interactive relationships as connections; (2) attribute graphs: classes and attributes as nodes, with connections between the classes and their attributes; and (3) schema graphs: attributes as nodes and their interactive relationships inside and outside the tables as links. The analysed class diagrams were stored in XMI, and therefore transformed with XSLT to the Pajek network format. Thereafter, small-world and scale-free analyses as well as a robustness analysis were performed on the graphs.  The results from the scale-free analyses showed no strict power-laws. Nevertheless, the classes’ relationships and attributes, and the betweenness in the schema graphs were long-tailed distributed. Furthermore, the schema graphs had small-world properties, and the analysed class and schema graphs were robust against errors but fragile against attacks. In a network structure perspective, these results indicate that false updates on random tables of a database should usually do little harm, but falsely updating the most central cells or tables may cause big damage. Consequently, it may be necessary to monitor and constrain sensitive cells and tables in order to protect them from attacks