Abstract: The present disclosure provides for using machine learning to evaluate flight demographics and predict part consumption. Historical aircraft part consumption data indicating prior consumption of aircraft parts is accessed, and historical aircraft flight demographics associated with the historical aircraft part consumption data are determined. A machine learning model is trained based on the historical aircraft part consumption data and the historical aircraft flight demographics, and the machine learning model is deployed to predict future aircraft part consumption.

Abstract: A method for flight data object realization includes storing a graph database that defines nodes populated with properties, receiving relationships among data items stored in data stores, transforming the relationships into edges in the graph database, receiving a query to create the flight data object, accessing the graph database, extracting particular properties from the graph database in response to the query, fetching from the data stores particular data items in response to the particular properties, generating the flight data object with the particular data items, creating a visual presentation of the flight data object, and presenting the flight data object.

Abstract: A method for flight data object realization includes storing a graph database that defines nodes populated with properties, receiving relationships among data items stored in data stores, transforming the relationships into edges in the graph database, receiving a query to create the flight data object, accessing the graph database, extracting particular properties from the graph database in response to the query, fetching from the data stores particular data items in response to the particular properties, generating the flight data object with the particular data items, creating a visual presentation of the flight data object, and presenting the flight data object.

Abstract: A method includes receiving, at one or more processors, airport runway geospatial data indicating a geospatial area associated with an airport runway and an altitude associated with the airport runway. The method also includes receiving aircraft location data associated with an aircraft. The aircraft location data indicates a position of the aircraft at different times and an altitude of the aircraft at different times. The method further includes determining whether the aircraft performed a touch-and-go operation on the airport runway based on the airport runway geospatial data and the aircraft location data. The touch-and-go operation includes the aircraft contacting the airport runway and subsequently taking off from the airport runway without stopping. The method also includes generating a notification in response to a determination that the aircraft performed the touch-and-go operation.

Abstract: The present disclosure provides for using machine learning to evaluate flight demographics and predict part consumption. Historical aircraft part consumption data indicating prior consumption of aircraft parts is accessed, and historical aircraft flight demographics associated with the historical aircraft part consumption data are determined. A machine learning model is trained based on the historical aircraft part consumption data and the historical aircraft flight demographics, and the machine learning model is deployed to predict future aircraft part consumption.

Abstract: A computing device is provided comprising a processor and a memory storing instructions executable by the processor. The instructions are executable by the processor to monitor location information for a plurality of aircraft in a geographic region. Geospatial data is received for the geographic region. The geospatial data comprises, for each of one or more physical objects in the geographic region, a virtual representation of the physical object. The instructions are further executable to determine that the location information meets a threshold difference compared to expected location information for a selected virtual representation of a physical object. An indication of an anomaly is output based at least on the determination that the location information meets the threshold difference.

Abstract: A method includes detecting, by a processing circuit, a high-speed rejected takeoff has occurred by determining an aircraft has accelerated to at least a first preset indicated airspeed value and then by determining the aircraft has decelerated below at least a second preset indicated airspeed value and the aircraft is on the ground. The method also includes detecting, by the processing circuit, an event other than the high-speed rejected takeoff has occurred by determining the aircraft has not accelerated to at least the first preset indicated airspeed value, or by determining the aircraft has accelerated to at least the first preset indicated airspeed value and the aircraft has not decelerated below at least the second preset indicated airspeed value, or by determining the aircraft is airborne.

Abstract: A method includes detecting, by a processing circuit, a high-speed rejected takeoff has occurred by determining an aircraft has accelerated to at least a first preset indicated airspeed value and then by determining the aircraft has decelerated below at least a second preset indicated airspeed value and the aircraft is on the ground. The method also includes detecting, by the processing circuit, an event other than the high-speed rejected takeoff has occurred by determining the aircraft has not accelerated to at least the first preset indicated airspeed value, or by determining the aircraft has accelerated to at least the first preset indicated airspeed value and the aircraft has not decelerated below at least the second preset indicated airspeed value, or by determining the aircraft is airborne.

Abstract: A system for determining a turnaround time for an aircraft at an airport includes one or more processors, one or more geospatial databases storing geospatial data, and a memory coupled to the one or more processors and the one or more geospatial databases. The memory stores data into a database and program code that, when executed by the one or more processors, causes the system to monitor a wireless data stream indicating aircraft location data. In response to determining the aircraft is idle and is located within an area where a unique parking stand is located, the system establishes a current position timestamp as an on-block time. In response to determining the aircraft is moving out of the unique parking stand, the system determines a position timestamp when the aircraft was last idle, and sets the position timestamp collected when the aircraft was last idle as an off-block time.