  The cost of delays was estimated as 33 billion US dollars only in 2019 for
the US National Airspace System, a peak value following a growth trend in past
years. Aiming to address this huge inefficiency, we designed and developed a
novel Data Analytics and Machine Learning system, which aims at reducing delays
by proactively supporting re-routing decisions.
  Given a time interval up to a few days in the future, the system predicts if
a reroute advisory for a certain Air Route Traffic Control Center or for a
certain advisory identifier will be issued, which may impact the pertinent
routes. To deliver such predictions, the system uses historical reroute data,
collected from the System Wide Information Management (SWIM) data services
provided by the FAA, and weather data, provided by the US National Centers for
Environmental Prediction (NCEP). The data is huge in volume, and has many items
streamed at high velocity, uncorrelated and noisy. The system continuously
processes the incoming raw data and makes it available for the next step where
an interim data store is created and adaptively maintained for efficient query
processing. The resulting data is fed into an array of ML algorithms, which
compete for higher accuracy. The best performing algorithm is used in the final
prediction, generating the final results. Mean accuracy values higher than 90%
were obtained in our experiments with this system.
  Our algorithm divides the area of interest in units of aggregation and uses
temporal series of the aggregate measures of weather forecast parameters in
each geographical unit, in order to detect correlations with reroutes and where
they will most likely occur. Aiming at practical application, the system is
formed by a number of microservices, which are deployed in the cloud, making
the system distributed, scalable and highly available.
