Abstract: An airport surface semantic path representation and an outlier taxiing pattern detection method for airport operation management, semantic conversion is realized based on dynamic features in an aircraft taxiing process, and after combining with surface map topology information, an airport surface aircraft semantic path representation is obtained; road segment geographic information is used to allocate a road segment weight to improve a longest common sub-sequence method, thereby constructing a flight path similarity matrix and using hierarchical clustering to realize surface taxiing pattern recognition; finally, by calculating the internal similarity of the identified patterns, the maximum outlier for identifying outlier patterns is obtained; after calculating the similarity between subsequent input flight paths and each pattern, as well as comparing a corresponding outlier with the maximum reachable outlier, the prewarning of outlier flight paths is achieved, the safety of surface management is ensured, and

Abstract: A system, method, and computer-readable medium are disclosed that may generate staging data independently from the execution of control scripts, which process the staging data, improving the quality, efficiency, and speed of data processing systems. The staging data can be independently loaded, validated, and utilized across multiple control scripts, reducing redundancy in the loading of data and the overhead of executing separate data processing for each control script. The control scripts can be automatically validated, such as by verifying expected output data ranges. Additionally, the complexity of the control scripts can be reduced as the loading of data is not performed by the control scripts. The controls scripts can generate a variety of output data, such as an indication of impacted accounts, and provide notifications based on the output data. A variety of machine learning classifiers may automatically generate the staging data and validate the staging data and/or output data.

Abstract: Aspects described herein generally improve the quality, efficiency, and speed of data processing systems by generating staging data independently from the execution of control scripts which process the staging data. The staging data can be independently loaded, validated, and utilized across multiple control scripts, reducing redundancy in the loading of data and the overhead of executing separate data processing for each control script. The control scripts can be automatically validated, such as by verifying expected output data ranges. Additionally, the complexity of the control scripts can be reduced as the loading of data is not performed by the control scripts. The controls scripts can generate a variety of output data, such as an indication of impacted accounts, and provide notifications based on the output data. A variety of machine learning classifiers can be used to automatically generate the staging data and validate the staging data and/or output data.

Abstract: A system, method, and computer-readable medium are disclosed that generate staging data independently from the execution of control scripts, which process the staging data, improving the quality, efficiency, and speed of data processing systems. The staging data can be independently loaded, validated, and utilized across multiple control scripts, reducing redundancy in the loading of data and the overhead of executing separate data processing for each control script. The control scripts can be automatically validated, such as by verifying expected output data ranges. Additionally, the complexity of the control scripts can be reduced as the loading of data is not performed by the control scripts. The controls scripts can generate a variety of output data, such as an indication of impacted accounts, and provide notifications based on the output data. A variety of machine learning classifiers may automatically generate the staging data and validate the staging data and/or output data.

Abstract: Aspects described herein generally improve the quality, efficiency, and speed of data processing systems by generating staging data independently from the execution of control scripts which process the staging data. The staging data can be independently loaded, validated, and utilized across multiple control scripts, reducing redundancy in the loading of data and the overhead of executing separate data processing for each control script. The control scripts can be automatically validated, such as by verifying expected output data ranges. Additionally, the complexity of the control scripts can be reduced as the loading of data is not performed by the control scripts. The controls scripts can generate a variety of output data, such as an indication of impacted accounts, and provide notifications based on the output data. A variety of machine learning classifiers can be used to automatically generate the staging data and validate the staging data and/or output data.

Abstract: Aspects described herein generally improve the quality, efficiency, and speed of data processing systems by generating staging data independently from the execution of control scripts which process the staging data. The staging data can be independently loaded, validated, and utilized across multiple control scripts, reducing redundancy in the loading of data and the overhead of executing separate data processing for each control script. The control scripts can be automatically validated, such as by verifying expected output data ranges. Additionally, the complexity of the control scripts can be reduced as the loading of data is not performed by the control scripts. The controls scripts can generate a variety of output data, such as an indication of impacted accounts, and provide notifications based on the output data. A variety of machine learning classifiers can be used to automatically generate the staging data and validate the staging data and/or output data.

Abstract: Aspects described herein generally improve the quality, efficiency, and speed of data processing systems by generating staging data independently from the execution of control scripts which process the staging data. The staging data can be independently loaded, validated, and utilized across multiple control scripts, reducing redundancy in the loading of data and the overhead of executing separate data processing for each control script. The control scripts can be automatically validated, such as by verifying expected output data ranges. Additionally, the complexity of the control scripts can be reduced as the loading of data is not performed by the control scripts. The controls scripts can generate a variety of output data, such as an indication of impacted accounts, and provide notifications based on the output data. A variety of machine learning classifiers can be used to automatically generate the staging data and validate the staging data and/or output data.