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.

Abstract: A dataset profiling tool configured to identify unique and non-unique column combinations in a dataset which includes a plurality of tuples, the tool including: an inserts handler module configured to: receive one or more new tuples for insertion into the dataset, receive one or more minimal uniques and one or more maximal non-uniques for the dataset, identify and group, for each minimal unique, any tuples of the dataset and any of the one or more new tuples which contain duplicate values in the column combinations of the minimal unique, to form grouped tuples which are grouped according to the minimal unique to which the tuples relate, validate the grouped tuples to identify supersets of the minimal uniques for which duplicate values were identified, to generate a new set of one or more minimal uniques and one or more maximal non-uniques, and output the new set of one or more updated minimal uniques and one or more maximal non-uniques.

Abstract: The optimized inequality join method is a method for joining relational tables on input inequality conditions. The optimized inequality join method is a relatively fast inequality join method using permutation arrays to store positional information for sorted attributed values. Additionally, space efficient bit arrays are used to enable optimization, such as Bloom filter indices, thus providing faster computation of the join results. The method may be used, for example, for joining various inequalities associated with a variety of measured environmental conditions for raising an alarm when certain conditions are met.

Abstract: The optimized inequality join method is a method for joining relational tables on input inequality conditions. The optimized inequality join method is a relatively fast inequality join method using permutation arrays to store positional information for sorted attributed values. Additionally, space efficient bit arrays are used to enable optimization, such as Bloom filter indices, thus providing faster computation of the join results. The method may be used, for example, for joining various inequalities associated with a variety of measured environmental conditions for raising an alarm when certain conditions are met.

Abstract: A dataset profiling tool configured to identify unique and non-unique column combinations in a dataset which comprises a plurality of tuples, the tool including: an inserts handler module configured to: receive one or more new tuples for insertion into the dataset, receive one or more minimal uniques and one or more maximal non-uniques for the dataset, identify and group, for each minimal unique, any tuples of the dataset and any of the one or more new tuples which contain duplicate values in the column combinations of the minimal unique, to form grouped tuples which are grouped according to the minimal unique to which the tuples relate, validate the grouped tuples to identify supersets of the minimal uniques for which duplicate values were identified, to generate a new set of one or more minimal uniques and one or more maximal non-uniques, and output the new set of one or more updated minimal uniques and one or more maximal non-uniques.

Abstract: A method of processing data stored in a database which comprises a plurality of rows and columns, the method comprising identifying a plurality of sets of column combinations, each set of column combinations comprising an identifier of at least one column allocating each set of column combinations to one of a plurality of nodes mapping the nodes to a lattice structure in which the nodes are connected in a superset or subset relationship according to the set of column combinations of each node selecting a current node processing the data in the set of columns of the current node to detect if the column combination is unique or non-unique traversing the lattice to a next node which is connected to the current node processing the data in the set of columns of the next node to detect if the column combination of the next node is unique or non-unique; and storing a record of whether each processed set of column combinations is unique or non-unique.

Abstract: A method for storing data in a replicated data storage system according to the invention comprises the steps of: partitioning the data into data blocks; and storing multiple replicas of a data block in a machine readable medium.