Abstract: The present disclosure provides a cargo revenue management system and method that increases the efficiency of cargo revenue management by increasing the prediction accuracy of cargo volumes that customers will tender in order to generate more efficient decisions to accept or reject cargo bookings. The provided system accomplishes this increased efficiency by identifying cargo volumes that customers arbitrarily book when an actual volume is unknown as disguised missing values and deemphasizing such values in the prediction of a cargo volume that will be received. The provided system additionally utilizes machine-learning models trained on a combination of features to predict a cargo volume that will be received for a particular cargo booking. Based on the predicted cargo volume that will be received, the system generates a decision of whether to accept or reject the cargo booking to maximize revenue generation.

Abstract: The present disclosure generally relates to a cost-based optimizer for efficiently processing data through the use of multiple different data processing platforms. The cost-based optimizer may receive an input plan for processing data that includes a number of base operators. The cost-based optimizer may then determine execution operators for each base operator, where each execution operator corresponds to a different data processing platform. From the execution operators, the cost-based optimizer may determine possible subplans for executing the input plan on one or more data processing platforms. The cost-based optimizer may determine the cost of executing each possible subplan and choose the subplan with the lowest cost.

Abstract: The present disclosure provides a cargo revenue management system and method that increases the efficiency of cargo revenue management by increasing the prediction accuracy of cargo volumes that customers will tender in order to generate more efficient decisions to accept or reject cargo bookings. The provided system accomplishes this increased efficiency by identifying cargo volumes that customers arbitrarily book when an actual volume is unknown as disguised missing values and deemphasizing such values in the prediction of a cargo volume that will be received. The provided system additionally utilizes machine-learning models trained on a combination of features to predict a cargo volume that will be received for a particular cargo booking. Based on the predicted cargo volume that will be received, the system generates a decision of whether to accept or reject the cargo booking to maximize revenue generation.

Abstract: The present disclosure generally relates to a cost-based optimizer for efficiently processing data through the use of multiple different data processing platforms. The cost-based optimizer may receive an input plan for processing data that includes a number of base operators. The cost-based optimizer may then determine execution operators for each base operator, where each execution operator corresponds to a different data processing platform. From the execution operators, the cost-based optimizer may determine possible subplans for executing the input plan on one or more data processing platforms. The cost-based optimizer may determine the cost of executing each possible subplan and choose the subplan with the lowest cost.