Aviation graphs

Aviation, Big Data, Curated, Machine Learning, Quantum Computing, Visualisation


At gluoNNet, we have been developing the processing and structuring of aviation data including airplane positioning (ADS-B) data to track, analyse, and manage flight tracks, aircraft behaviour, and flight efficiency. In collaboration with the Aviation Administration of Kazakhstan (CAAKZ) and BusinessOptix, we have developed a proof-of-concept as part of a wider programme to create a truly Digital Regulator moving from reactive to proactive regulation of the aviation fleets. The team has just completed a Pilot phase and is called ‘Sunflower’. Using cutting-edge data-analysis methods, ‘Sunflower’ delivers the CAAKZ information about an aircraft’s position, flight history. Altogether, the ‘Sunflower’ regulation system will help the Kazakh aviation authorities in monitoring aircraft and national airspace, investigating incidents, and preventing illegal activities, eventually making aviation safer and more transparent.

We process satellite and other data using a scalable and optionally cloud-based architecture that tailors the data processing to the analysis requirements. We automatically populate a graph-based contextual database that contains elements such as flight times and points of origin and destination. In collaboration with CAAKZ, we are generating alerts for high priority use cases, centred around behaviours that are of most interest to the aviation authority. The data is visualised in a multi-panel, interactive UI, focussed on supporting human-initiated tasks which will later be encapsulated within automated alerting. We have visualised data for both multiple planes and single planes based on user selection criteria.

Planned work includes productionising the system whilst adding more sophisticated analyses, more regular alerting and processing, increasing the richness of the UI and the number of alerting conditions, as well as integrating the PoC into the aviation authorities’ digital ecosystem and that of some its airline operators.

In collaboration with CERN openlab, we explore particle-track reconstruction which poses striking similarities to the tracking of airplane routes. In both cases, there is a huge increase in complexity as the amount of data points increases. In this study, the reconstruction of airplane trajectories has two major aims: understanding incidents and understanding triggers of a deviation of flight path from the optimal route utilising quantum-computing techniques.