Air transportation systems can be modeled as several layers of complex networks. For instance, airport networks depicting how airlines operate their aircraft in order to connect two airports; air navigation route networks showing how aircraft are flying through the airspace. The multi-layer air transportation networks are strongly inter-dependent and coupled together, rather vulnerable and fragile under disruptions, and highly dynamic and they evolve over time. Therefore, it is critical to understand complex topological structures, robustness behaviors under disruptions, and temporal evolution of air transportation networks.
Maintaining robustness is a key challenge for present and future transportation systems. The analysis of network robustness is a time-demanding task, whose complexity increases with the size of networks. Accordingly, network attacks are often built on network metrics, for instance, attacking the nodes in decreasing order of their degree or betweenness. Albeit the results can be insightful, there is no guarantee regarding the quality or optimality of these attacks. We propose a new techniques for a computationally efficient attacking model. Our computationally efficient attacking model contributes to scalable analysis of robustness, not only for air transportation, but networks in general.
Storing and indexing huge collections of data is increasingly important in various fields. Accessing such quickly growing amount of data requires large disk arrays for storage and high-class servers for analysis. Both are very expensive. Compression is a key technology to cope with the increasing flood of big data - be it in the cloud or locally. Many analysis tasks require indexed access to the data to carry out pattern matching or more general data mining. In the future, such analysis of compressed “big data” becomes increasingly important. In our work, we addresse key challenges, taking data management problems from the Bioinformatics field and Transportation domain as use cases.